case study of air pollution in kerala

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Analysis of Air Pollution in Three Cities of Kerala by Using Air Quality Index

S.N Jyothi 1 , Kishan Kartha 1 , Divesh 1 , Adarsh Mohan 1 , Jithin Pai U 1 and Geena Prasad 1

Published under licence by IOP Publishing Ltd Journal of Physics: Conference Series , Volume 1362 , International Conference on Physics and Photonics Processes in Nano Sciences 20–22 June 2019, Eluru, India Citation S.N Jyothi et al 2019 J. Phys.: Conf. Ser. 1362 012110 DOI 10.1088/1742-6596/1362/1/012110

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1 Dept of Mechanical Engineering, Amrita Vishwa Vidyapeetham, Amritapuri, India

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Air pollution has an appalling effect on human health and our planet has a whole. This study quantifies air pollution using a parameter – Air Quality Index and compare the pollution of air in six major sites across Kerala, India (South over bridge and Eloor in Ernakulam district, Pettah and Veli in Thiruvananthapuram district, Chavara and Kadappakada in Kollam district). The significant differences in seasonal variation of pollutants in the three districts are also studied. The conclusion of the study shows: a) Air Quality Index in all these areas is predominantly determined by Particulate Matter concentration; b) Ernakulam district has the highest Air Quality Index among both industrial and residential areas and Kadappakada, Kollam has the lowest Air Quality Index among all the six areas studied; c) The air quality deteriorates during winter and summer because of limited pollutant dispersion.

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Phytoremediation for urban landscaping and air pollution control—a case study in Trivandrum city, Kerala, India

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• Published: 07 November 2020
• Volume 28 , pages 9979–9990, ( 2021 )

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• Ancy S Watson 1 &
• Sudha Bai R   ORCID: orcid.org/0000-0003-4834-7129 1  

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Air pollutant concentration of Trivandrum, the capital of Kerala, exceeded the limits of National Ambient Air Quality (NAAQ) standards, according to a study conducted in 2015 by NATPAC. These polluted corridors harbour vegetation on roadsides and traffic islands, planted solely for aesthetic appeal. Analysis of air pollution tolerance levels of existing plants can act as a scientific basis for efficient planning of the urban landscape. Sixty-seven species, including flowering, fruit-bearing, ornamental, shade-providing and timber-yielding species, were screened for their relative resistance to air pollution. Based on leaf pH, relative water content, chlorophyll and ascorbic acid levels, the Air Pollution Tolerance Indices (APTI) of each species were formulated and they were grouped into the following: tolerant, moderately tolerant, intermediate and sensitive groups. Agave americana (18.40), Cassia roxburghii (17.63), Anacardium occidentale (11.97), Cassia fistula (11.60), Mangifera indica (11.59) and Saraca asoca (10.88) may be considered for planting near green spaces like roundabouts and near pollution prone industrial areas, as they belong to tolerant category. Comparison of APTI during summer and monsoon also revealed the stability of Agave americana , Saraca asoca , Ficus benghalensis , Peltophorum pterocarpum , Ficus elastic a, Ixora finlaysoniana , Mangifera indica , Canna indica and Delonix regia in maintaining pollution tolerance even during water disparity. Agave americana , Anacardium occidentale , Ficus elastica , Mangifera indica , Syzygium cumini , Ficus benghalensis , Nerium oleander and Ficus benjamina were found to be suited for mass planting, as was evident from their Anticipated Performance Indices (API).

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Acknowledgements

Facilities: The Principal, University College, University of Kerala, Trivandrum.

Secondary data: Kerala State Pollution Control Board (KSPCB), Kerala Forest Department (KFD) and National Transportation Planning and Research Centre (NATPAC).

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Availed by: Ancy S. Watson

Purpose: Research Grant

Kerala Forest Department Fund (KFDF)

Availed by: Sudha Bai R.

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Watson, A.S., Bai R, S. Phytoremediation for urban landscaping and air pollution control—a case study in Trivandrum city, Kerala, India. Environ Sci Pollut Res 28 , 9979–9990 (2021). https://doi.org/10.1007/s11356-020-11131-1

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DOI : https://doi.org/10.1007/s11356-020-11131-1

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• DOI: 10.1007/s11356-020-11131-1
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Phytoremediation for urban landscaping and air pollution control—a case study in Trivandrum city, Kerala, India

• Ancy S. Watson , Sudha Bai R
• Published in Environmental science and… 7 November 2020
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Health impact of ambient air pollution in Kerala, India. A first quantitative risk assessment: Myriam Tobollik

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M Tobollik, D Plaß, D Wintermeyer, Health impact of ambient air pollution in Kerala, India. A first quantitative risk assessment: Myriam Tobollik, European Journal of Public Health , Volume 25, Issue suppl_3, October 2015, ckv175.119, https://doi.org/10.1093/eurpub/ckv175.119

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Globally, ambient air pollution is an important risk factor for human health. Particularly in South Asia, this risk factor causes a considerable disease burden. For developing effective mitigation programs small-scale health risk assessments are needed to quantify the effects of ambient air pollution on health. The objective of the study is to test the feasibility of the environmental burden of disease (EBD) concept at state level in India and to quantify a first set of disease burden estimates due to ambient air pollution in urban areas of Kerala.

Particulate Matter (PM) is used as an indicator for ambient air pollution. The disease burden due to PM2.5 is quantified in Years of Life Lost (YLL) for the female and male population aged 30 years and older living in urban areas of Kerala. Scenario analyses are performed to account for uncertainties in the input parameters.

About 6,100 (Uncertainty Interval (UI): 4,150–7,790) total natural deaths can be attributed to PM, resulting in about 96,000 (UI: 65,000–123,000) YLLs due to premature mortality (base case scenario, average for 2008–2011). Depending on the underlying assumptions the results vary between 47,000 and 377,000 YLLs. Scenario analyses show that a decrease of 10% in PM concentration would save around 16,000 (UI: 11,000–20,000) life years and an increase by 10% would elevate the burden by nearly 13,000 (UI: 9,000–16,000) YLLs. A sensitivity analysis shows that around half of the disease burden caused by PM is due to cardiovascular causes.

The results foster awareness about air quality standards at local level and can support decision-making processes aiming at cleaner and healthier environments and improved health. Besides some limitations due to lack of data the EBD-concept is applicable at state level. The assessment is limited to effects of ambient air pollution. However, indoor air pollution is an additional health risk in India which needs to be assessed for a comprehensive quantification of disease burden due to air pollution.

Key messages

The environmental burden of disease concept is applicable at state level for Kerala, India

In the base case scenario about 96,000 (UI: 65,000–123,000) Years of Life Lost (YLL) due to premature mortality can be attributed to ambient particulate matter

• air pollution
• risk assessment

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Ambient air quality of a less industrialized region of India (Kerala) during the COVID-19 lockdown

Jobin thomas.

a Department of Civil Engineering, Indian Institute of Technology Madras, Chennai, 600 036, India

P.J. Jainet

b KSCSTE-Centre for Water Resources Development and Management, Kozhikode, 673 571, Kerala, India

K.P. Sudheer

c Kerala State Pollution Control Board, Thiruvananthapuram, 695 004, Kerala, India

d Kerala State Council for Science, Technology and Environment, Thiruvananthapuram, 695 004, Kerala, India

e Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN, USA

Associated Data

Graphical abstract.

• • Air quality of major cities of Kerala State (India) during the COVID-19 lockdown.
• • Substantial reduction of NO 2 (-48 %) NO x (-53 % to -90 %) and CO (-24 % to -67 %).
• • Significant decrease in PM 2.5 (-24 % to -47 %) and PM 10 (-17 % to -20 %) levels.
• • Increase of O 3 levels in Northern Kerala during lockdown period.
• • Indications of multiple sources (other than traffic and industry) of air pollution.

This study assesses the effect of lockdown, due to the coronavirus disease (COVID-19) pandemic, on the concentration of different air pollutants and overall air quality of a less industrialized region (Kerala) of India. We analysed data from four ambient air quality stations over three years (January to May, 2018–2020) with pairwise comparisons, trend analysis, etc. Results indicated unprecedented reduction in the concentration of the air pollutants: nitrogen dioxide, NO 2 (-48 %), oxides of nitrogen, NO x (-53 % to -90 %), carbon monoxide, CO (-24 % to -67 %) and the particulate matter (-24 % to -47 % for particulate matter with a diameter of less than 2.5 μm, PM 2.5 ; -17 % to -20 % for particulate matter with a diameter of less than 10 μm, PM 10 ), as well as the reduction of the National Air Quality Index (NAQI). These reductions indicate an overall improvement of the ambient air quality due to restrictions on transportation, construction, and the industrial sectors during lockdown, even in an area considered less industrial. Despite the general decreasing trend of the concentration of various air pollutants from January to May, suggesting seasonal influences, the trend was intensified in the year 2020 due to the added effect of the lockdown measures. Comparison of the results with those from larger and more industrialized cities suggests that the effects of lockdown are more variable, and focused on the levels of gaseous pollutants. Findings from this study demonstrate the far-reaching effects and implications of the COVID-19 lockdown on ambient air quality, even on less industrialized and less urbanized regions.

1. Introduction

The rapid spread of the coronavirus disease (COVID-19) pandemic caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) induced inenarrable social and economic impacts across the globe. The COVID-19 was first detected in Wuhan (China) and the disease had affected more than 15 million people in 216 countries until 26 July 2020 ( https://covid19.who.int ). The first COVID-19 case in India was reported on 30 January 2020 in Kerala State, followed by a few more in the first week of February 2020, with escalating cases since the second week of March 2020. As of 26 July 2020, the Government of India had reported more than 1.4 million confirmed cases and 32,771 deaths ( https://www.mygov.in/covid-19 ). Kerala was one of the most affected states of India during the initial stages of the COVID-19 outbreak in the country.

In response to the COVID-19 outbreak, various countries implemented diverse non-pharmaceutical interventions (e.g., personal protection and hygiene, physical distancing, environmental and travel-related measures, etc.) to slow down and to reduce the mortality rates associated with the COVID-19, with the ultimate objective to reach and to maintain the state of low-level or no transmission. In addition to these efforts, many countries imposed large-scale public health and social measures, including restriction of private and public transportation, suspension of educational/commercial/business/religious activities, and geographical area quarantine (often collectively referred to as lockdown) to curtail the spread of the highly transmissible virus within the society ( World Health Organisation, 2020 ). The preventive lockdown was first implemented in Wuhan on 23 January 2020 and subsequently extended for entire China for at least three weeks ( Le et al., 2020 ). Later, several countries across the globe (e.g., Belgium, Italy, Spain, Germany, UK, South Africa, Argentina, Colombia, USA, Israel, New Zealand) implemented the lockdown for varying periods.

Kerala (India) implemented the state-wide lockdown on 24 March 2020, as numbers of COVID-19 cases drastically increased in the State (Table S1; Fig. 1 ). The Government of India also enforced the nation-wide lockdown from 25 March 2020 onwards. Later, the nation-wide lockdown was extended further till the end of May 2020, with varying exemptions in different parts of the country. The lockdown implemented in Kerala was effective against the transmission of the virus, as the COVID-19 cases (registered during March, April and the first week of May) were almost completely recovered by 9 May 2020 ( Fig. 1 ). The rising number of cases since 10 May 2020, however, was attributed to the controlled re-entry of the natives of Kerala stranded in other parts of the country and the globe. As of 26 July 2020, the State Government of Kerala had reported 19,025 confirmed cases and 61 deaths ( https://dashboard.kerala.gov.in ). Implementation of the lockdown restrictions has resulted in the stagnation of economic activities, such as construction, industrial projects, transportation, etc., leading to rising unemployment, decrease in income generation, and reduced consumer activity. The growth rate of the Gross Domestic Product (GDP) of India in the fourth quarter of the fiscal year 2020 dropped to 3.1 % compared to the previous quarters (4.1–5.2 %), mainly due to the effect of the COVID-19 on the country’s economy ( http://mospi.gov.in ).

History of COVID-19 cases of Kerala (up to 31 May 2020). The period between the dashed vertical lines indicates the state-wide lockdown phase in Kerala.

Despite the infliction on the economic sector, the lockdown measures have unintentionally bring forth benefits for the environment. During the lockdown period, across the globe, emissions from the transportation and industrial sectors were remarkably lower leading to a significant reduction in the levels of environmental pollution ( Arora et al., 2020 ; Muhammad et al., 2020 ; Paital, 2020 ). Numerous researchers (e.g., Collivignarelli et al., 2020 ; Dantas et al., 2020 ; He et al., 2020 ; Kanniah et al., 2020 ; Chin et al., 2020 ) have analysed data on environmental pollution (mostly air pollution) of different parts of the globe and have reported improvement in the ambient air quality levels during the COVID-19 lockdown. For India, researchers have similarly quantified the impacts of the nation-wide lockdown on the ambient air quality, using observed data from the ambient air quality monitoring stations ( Mahato et al., 2020 ; Sharma et al., 2020 ) as well as satellite-derived/reanalysis products ( Gautam, 2020 ) or a combination of both ( Lokhandwala and Gautam, 2020 ; Mahato and Ghosh, 2020 ; Selvam et al., 2020 ).

While many studies have documented improvements in air quality in major cities, the effects of lockdown measures on ambient air quality of less urbanized and less industrialized regions are not investigated comprehensively at the global level. Whereas urban and industrialized areas generally show higher levels of pollution compared to less urbanized and less industrialized areas, with air quality improving in less urban (or rural) areas ( Strosnider et al., 2017 ), contrasting patterns in some air pollutants (e.g., SO 2 ) between urban and rural areas during the COVID-19 lockdown have also been reported ( Wang et al., 2020 ). Moreover, research has also not addressed the effect of short-term fluctuations and seasonal trends in the concentrations of air pollutants due to differences in seasons between the lockdown (i.e., summer) and pre-lockdown (i.e., winter) periods in a broader regional context. Hence, this study investigated the ambient air quality in a less industrialized region (the maritime State of Kerala) of India.

The study sought to answer the following research questions. (1) To what extent did the lockdown during the COVID-19 improve the ambient air quality of Kerala State? (2) How significant are the seasonal trends in the reduction of air pollutant levels, and how different are their influences in 2020 compared to the previous years? (3) How do changes in the ambient air quality of Kerala State differ from more urbanized and industrialized areas? We hypothesized that significant reductions in air pollution would emerge from the lockdown measures, even in the less industrialized state of Kerala, though the detailed trends may differ from those of the more urbanized and industrialized areas.

2. The study area of Kerala

The Kerala State is an elongated strip of land, along the windward side of the Western Ghats, in the southwest tip of Peninsular India ( Fig. 2 ). The State covers an area of 38,863 km 2 (roughly the size of Switzerland), which accounts for about 1.2 % of the total geographical area of India. The physiography of the State is classified into the highland (in the east), the lowland (in the west) and the midland in between. The majority of the area of Kerala experiences tropical humid climate, while the eastern slopes of the Western Ghats fall under tropical semi-arid climate. Kerala receives a mean annual rainfall of about 2800 mm, of which, 70 % occurs as the Indian summer monsoon rainfall ( Thomas and Prasannakumar, 2016 ). The State, though small in size, has varying topographical and climatic conditions leading to a wide spectrum of land use/ land cover and cropping pattern. However, roughly 60 % of the land area is utilized for agricultural purposes, whereas nearly 30 % of the State is under forest cover ( http://www.forest.kerala.gov.in/index.php/forest/forest-area ). Kerala State witnessed recurrent floods in 2018, 2019 and 2020, where the August 2018 flood occurred in Kerala was the worst flood since 1924. The August 2018 flood primarily affected the central parts of the State and inundated about 1100 km 2 in Palakkad, Thrissur, Ernakulam, Idukki, Kottayam, Alappuzha, and Pathanamthitta districts. The flood had severely affected the croplands (∼46 %) and settlement areas (∼21 %) of these districts ( Lal et al., 2020a ). The census 2011 data ( https://censusindia.gov.in ) indicate that the population of Kerala as 33,387,677 (population density = 859 persons km −2 ), which forms roughly 3 % of the population of India. The major urban areas of the State include Thiruvananthapuram, Ernakulam and Kozhikode, with a population of 0.97, 0.60 and 0.61 million, respectively.

Map of Kerala State (India) including the locations of the ambient air quality monitoring stations.

In general, the levels of air pollutants (except particulate matter) in Kerala are within the permissible limit prescribed by the national ambient air quality standards ( http://kerenvis.nic.in ). The concentration of various air pollutants, especially particulate matter, however, in Ernakulam and Kozhikode districts was reported nearing the alarming level in the recent years ( Government of Kerala, 2020a ). Tobollik et al. (2015) observed a considerable health burden for the population living in urban Kerala mainly due to particulate matter with a diameter of less than 2.5 μm and 10 μm (PM 2.5 and PM 10 ) concentrations, albeit local air quality standards being met, and reported that a decrease of 10 % in particulate matter concentrations may save 15,904 (95 % confidence interval: 11,090-19,806) life years. The industries and vehicular traffic are the major drivers for the deterioration of air quality of the State. The industrial sector of Kerala is not well-developed compared to other states of India, which is evinced by the substantially lower figures of the per-capita domestic product and per-capita manufacturing value added in the State ( Thomas, 2005 ). Among the different states of India, Kerala ranks 12 th position in terms of the total number of industries and 18 th place in terms of the fixed capital ( http://mospi.nic.in ). Based on the annual survey of industries of 2016-17, 6507 industries were operating in the manufacturing sector of Kerala, where more than 60 % of the industries is related to food, non-metallic minerals, tobacco, wood, rubber and plastic products ( Government of Kerala, 2020b ). Among the different districts of the State, Ernakulam has the greatest number of industries (20 %), where the two dominant industrial clusters (consisting mostly of chemical industries) of the district are Eloor and Ambalamugal.

The major transport infrastructure of the State includes 0.27 million km of road, 1588 km of railway, 1687 km of inland waterway, 585 km coastal route with 18 ports and four international airports. The road density of Kerala is 3.9 km km −2 , which is roughly three times the national average. The primary road network of the State is the National highways, which handle about 40 % of the total traffic, while the State highways and the major district roads carry another 40 %. The traffic on the roads of the State shows a steadily increasing rate of 12–14% per year. As of March 2019, Kerala had more than 13 million registered motor vehicles and the last two decades witnessed a compounded annual growth rate of above 10 % ( Government of Kerala, 2020a ).

3. Materials and methods

The ambient air quality data of four air quality monitoring stations in Kerala, viz., Plamood, Thiruvananthapuram (K1), MG Road, Ernakulam (K2), Eloor, Ernakulam (K3) and Palayam, Kozhikode (K4) were collected from the Kerala State Pollution Control Board ( Fig. 2 ). The ambient air quality data analyzed include gaseous pollutants, such as nitrogen monoxide (NO), nitrogen dioxide (NO 2 ), oxides of nitrogen (NO x ), ammonia (NH 3 ), sulphur dioxide (SO 2 ), carbon monoxide (CO), and ozone (O 3 ) as well as particulate matter (PM 2.5 and PM 10 ). Measurements of all air pollutants were not available, however, from all the monitoring stations ( Table 1 ). Data for daily ambient air quality of all the stations were pre-processed to remove the outliers. We computed the National Air Quality Index (NAQI) (after Central Pollution Control Board, 2014 ) using the processed air quality data. The NAQI uses NO 2 , NH 3 , SO 2 , CO, O 3 , PM 2.5 , PM 10 and lead (Pb) for computing the index. The index requires the concentration of a minimum of three pollutants, including at least PM 2.5 or PM 10 . We converted the concentration of each air pollutant (C p ) to a normalized number (i.e., sub-index, I p ) using segmented linear functions (Eq. 1).

where, B HI is the breakpoint concentration greater or equal to C p , B LO is the breakpoint concentration smaller or equal to C p , I HI and I LO are the air quality index values corresponding to B HI and B LO , respectively. Finally, Eq. 2 provides an estimation of the NAQI ( Central Pollution Control Board, 2014 ):

Details of the ambient air quality monitoring stations, Kerala*.

*Details belong to the ambient air quality monitoring stations under the scope of this study; ** Data available in 2020.

The NAQI contains six classes: good (0–50), satisfactory (51–100), moderate (101–200), poor (201–300), very poor (301–400) and severe (401–500).

The analysis utilized daily data (24 h) from 1 January to 31 May of 2018, 2019 and 2020. We considered the period between 1 January and 23 March as the pre-lockdown period, and the duration from 24 March to 17 May as the lockdown period. Following this classification, the data between 1 January 2020 and 23 March 2020 represent the pre-lockdown period (PLD) 2020, while the data from 24 March 2020 to 17 May 2020 are termed as the lockdown (LD) 2020. Similarly, we considered the daily average of the air quality data between 1 January and 23 March of 2018 and 2019 as the PLD mean , and we treated the daily average of the data between 24 March and 17 May as the LD mean . We computed descriptive statistics of the different air pollutants for the PLD 2020, LD 2020, PLD mean and LD mean for pairwise comparisons. Since the air quality data rarely follow the normal distribution, we applied the Mann-Whitney test for analysis of statistical significances between groups, as the Mann-Whitney test is applicable for differences in medians as well as in the shape and spread of the distributions.

We tested the daily time series of each pollutant for the entire period (i.e., 1 January to 31 May) of all years (i.e., 2018, 2019 and 2020) for presence of monotonic trends using the Mann-Kendall test ( Kendall, 1975 ; Mann, 1945 ). Since the Mann-Kendall test assumes that the data are independent and identically distributed, we tested the data series for serial correlation. We applied the Mann-Kendall test to the original time series, when a significant autocorrelation lacks at the 5 % level, and to the pre-whitened series, when a significant serial correlation occurrs ( von Storch, 1999 ).

We compared changes in the ambient air quality data during the LD 2020 in Kerala State with the changes in the levels of various air pollutants recorded in the urbanized and industrialized regions of India as well as across the globe during the lockdown period. This comparison evaluated differences in the magnitude of changes between the urbanized/industrialized and less urbanized/industrialized regions.

The various air pollutants measured at the different ambient air quality monitoring stations of Kerala show notable temporal variability during January 2020 to May 2020 ( Fig. 3 ). The wider temporal variations are noted in PM 2.5 and PM 10 at K1, NO x and NH 3 at K2, NO, NO 2 , NO x and NH 3 at K3 and CO at K4. In general, all the air quality monitoring stations of Kerala witnessed a gradual reduction in the concentration of most of the air pollutants during the lockdown window.

Daily time series (1 January 2020 - 31 May 2020) of the concentration of different air pollutants measured at (a) K1, (b) K2, (c) K3 and (d) K4. The period between the dashed vertical lines indicates the state-wide lockdown phase in Kerala.

4.1. Comparison of ambient air quality during pre-lockdown and lockdown phases (PLD 2020 vs. LD 2020)

Table 2 provides the summary statistics of the ambient air quality data of the air quality monitoring stations (K1, K2, K3 and K4) of Kerala. A comparison of the air quality data during the PLD 2020 and LD 2020 (Fig. S1) shows significant variability in the concentration of most of the air pollutants between the periods (p ≤ 0.001). The average of the particulate matter shows considerable differences between the PLD 2020 and LD 2020 at K1 (PM 2.5 = -71 % and PM 10 = -45 %) and K2 (PM 2.5 = -58 % and PM 10 = -49 %), whereas the differences at K3 are comparatively lower in magnitude ( Table 2 ). The gaseous pollutants show noticeable changes at K3 and K4 compared to K1 and K2 (except for NO x ) (Fig. S1). The average concentration of the air pollutants, such as NH 3 , NO 2 , NO x and CO measured at K3 during the LD 2020 is lesser than 50 % of their concentration during the PLD 2020. A similar change is visible in the concentration of NO x , CO and SO 2 at K4. The significant changes in the concentration of the air pollutants, such as NO (K1 = -21 %, K3 = -28 %), NO 2 (K3 = -70 %), NO x (K2= -90 %, K3 = -65 %, K4 = -71 %) and CO (K1 = -24 %, K2 = -10 %, K3 = -50 %, K4 = -75 %) imply reduced emissions from the transportation and industrial sectors due to the lockdown.

Summary statistics of various air pollutants of different stations (2a to 2d) during the pre-lockdown and lockdown periods.

The air pollutants, such as NO 2 , NH 3 , SO 2 , CO, O 3 , PM 2.5 , PM 10 , and Pb, are considered for the calculation of the NAQI ( Central Pollution Control Board, 2014 ). In this study, however, all the air pollutants except Pb were used for the computation of the NAQI. The temporal variability of the NAQI of all the stations shows significant differences between the PLD 2020 and LD 2020 (p ≤ 0.001) ( Fig. 4 ). On average, the NAQI of K2 and K4 during the LD 2020 shows a reduction by 43 %, whereas the NAQI of K1 is decreased by 26 % compared to the NAQI of the PLD 2020 ( Table 2 ). The reduction in the NAQI implies an improvement in the overall air quality. Following the classification of the NAQI, the PLD 2020 is characterized by moderate to satisfactory air quality in all the stations, whereas the LD 2020 is dominated by good to satisfactory air quality. All the monitoring stations do follow this trend, whereas difference in the NAQI between the periods is less at K3 compared to other stations. Although most of the air pollutants show a significant decrease in their concentration during the LD 2020 (compared to the PLD 2020), O 3 at K4 exhibits a significantly higher concentration during the LD 2020 (p ≤ 0.001) (Fig. S1d). Comparison of the average concentration of the air pollutants, as well as the average NAQI, indicates that the air quality of the major cities of Kerala during the LD 2020 is significantly different compared to the PLD 2020.

Daily NAQI between 1 January 2020 and 31 May 2020 computed at (a) K1, (b) K2, (c) K3 and (d) K4. The horizontal lines represent the threshold of different NAQI classes.

4.2. Trend of air pollutants between January and May (2018, 2019 and 2020)

Although the LD 2020 showed a decrease of the concentration of the air pollutants in Kerala State, the reduction in the concentration (due to lockdown) cannot be determined without estimating the role of short-term fluctuations or the seasonal trends in the concentration of the air pollutants during the analysis period. The fluctuations can relate to meteorological, seasonal or even anthropogenic and cultural factors ( Chen et al., 2015 ; Cichowicz et al., 2017 ; Mohtar et al., 2018 ). Zangari et al. (2020) observed hardly any significant changes in the concentration of PM 2.5 and NO 2 in New York City during the lockdown period compared to the same period in 2015-2019. Hence, we tested the daily time series of the ambient air quality data of the stations from January to May of 2018, 2019 and 2020 for the presence of trends.

Results of the trend analysis of the concentration of the air pollutants of the various stations indicate a significant decreasing trend over the analysis period in 2020 ( Table 3 ). The concentration of all the air pollutants at K1, K2 (except CO), K3 (except PM 2.5 and PM 10 ) and K4 (except O 3 , NH 3 and SO 2 ) shows significant negative trends (95 % confidence levels), implying a gradual decrease of the concentration from January to May. On the other hand, the concentration of O 3 at K4 shows a significant increasing trend. A comparison of the trends of the concentration of the air pollutants in 2020 with the trends in 2018 and 2019 indicates that most of the air pollutants follow a decreasing trend in all the years (e.g., PM 2.5 , PM 10 at K1, K2 and K4, NH 3 at K1 and K4), whereas a few exhibit alternating trends (e.g., NO x at K2 and K3, CO at K1 and K3), and a few show reversal of the trends (SO 2 and CO at K2, O 3 at K4) in 2020.

Results of trend analysis of pollutant levels of various stations during January 1 to May 31 of 2018, 2019 and 2020.

ND-Not tested due to multiple data gaps.

Confidence levels: 95 % (*), 99 % (**) and 99.9 % (***).

The continuously decreasing trend of the concentration of the air pollutants (e.g., CO, PM 2.5 and PM 10 ) over the period (i.e., from January to May) is likely due to the reduction of the number of vehicles during April and May, as a result of the closure of the educational institutes and related activities for summer holidays. Besides, the winter months (January and February) typically have higher levels of air pollution compared to the summer season due to the decreased levels of solar flux and photochemical activity and fewer instances of long-range pollutant transport ( Zangari et al., 2020 ). Since there a significant decreasing trend exists for most of the pollutants in all the years, it could mean that any changes recorded in the concentration of the pollutants between the PLD 2020 and LD 2020 could have resulted from this general trend, as well as the effect of lockdown. Hence, reversal of the positive trend, as well as the intensification of the general negative trend (manifested in Mann Kendall test statistic) in 2020 compared to the previous years, may be treated as the effect of lockdown.

An estimation of the number of days in 2018–2020, in which the concentration of the different air pollutants exceeding the national ambient air quality standards, provides additional details. The gaseous pollutants showed very few exceedance during the years (except NO 2 for a few days at K3), whereas the particulate matter exceeded the limits for a significantly greater number of days. The national ambient air quality standards for PM 2.5 and PM 10 (24 h) are 60 and 100 μg m −3 , respectively. The number of days (during the pre-lockdown time frame) exceeding the national standards in 2018 and 2019 is remarkably higher than that in 2020 for both PM 2.5 and PM 10 (Fig. S2). At K1, for example, 32 % of the total days between 1 January 2018 and 23 March 2018 (PLD 2018) and 22 % of the total days between 1 January 2019 and 23 March 2019 (PLD 2019) exceeded the national standard for PM 2.5 , while during the PLD 2020, only 7 % of the total days surpassed the standard. During the LD 2018 (24 March 2018 to 17 May 2018), LD 2019 (24 March 2019 to 17 May 2019) and LD 2020, however, none of the days exceeded the national standard for PM 2.5 . We note such a pattern in all the years and all the stations for both PM 2.5 and PM 10 . Figure S2 indicates that the number of days exceeding the national standards for PM 2.5 and PM 10 also shows a declining pattern from 2018 to 2020 for both the pre-lockdown and lockdown phases, but in differing magnitudes. As definite trends are notable within the concentration of the air pollutants during the analysis period, the estimation of the change in the concentration of the pollutants due to the effect of lockdown should consider this trend also.

4.3. Relative changes in the ambient air quality in Kerala during lockdown

Since the changes in the concentration of the various air pollutants during the LD 2020 result from the combined effect of the seasonal trends as well as the lockdown, we estimated the effect of lockdown on the ambient air quality as the relative change in the concentration of the air pollutant (ΔP x ) between the PLD 2020 and LD 2020 with respect to the corresponding average change of the previous years. This expression is (Eq. 3):

where, ΔP x is the relative change in the concentration of any given air pollutant during the LD 2020 with respect to the PLD 2020, P L D n o r m = P L D 2020 P L D m e a n and L D n o r m = L D 2020 L D m e a n .

The ΔP x of most of the air pollutants in all the stations implies a remarkable reduction of the concentration in the LD 2020 with respect to the PLD 2020 ( Fig. 5 ). The significant reduction in the concentration of the pollutants, such as NO 2 (-48 %) NO x (-53 % to -90 %), CO (-24 % to -67 %) as well as the particulate matter (-24 % to -47 % for PM 2.5 , -17 % to -20 % for PM 10 ) is correlated to the decreased emissions from the transportation and industrial sources during the lockdown period. One may notice that the rate of reduction in the concentration of the air pollutants is non-uniform across the State, implying the role of regional socio-economic, meteorological and anthropogenic factors controlling air quality.

Relative change (ΔP x ) in the concentration of the air pollutants during the LD 2020: (a) K1, (b) K2, (c) K3 and (d) K4. No data (or gap) indicates unavailability of pollutant data.

Among the different stations, K1 exhibits a comparatively lower percentage of reduction of the concentration of the various air pollutants ( Fig. 5 a). NO x and SO 2 concentrations at K2, NO x and NH 3 concentrations at K3, NO x , SO 2 , and CO levels at K4 show significant reduction (> 50 %) in their concentration during the LD 2020. The concentration of the particulate matter is reduced only at K1 and K2 ( Fig. 5 a, b), however, the concentration of the particulate matter in a few stations (K3 and PM 2.5 at K4) shows an increase during the LD 2020 ( Fig. 5 c, d). The relative change of the NAQI of the different stations suggests that the improvement in the air quality is significant at K2 (-38 %) and K4 (-15 %), whereas the change in the NAQI of K1 is insignificant. The relative change at K3 shows an increase (+43 %), however, which is probably due to the higher background and residual pollution.

5. Discussion

The improvement of the air quality of Kerala State during the LD 2020, though varying in magnitude, results from the combined effect of the lockdown measures (i.e., restrictions on the transportation sector and cessation of construction and industrial activities) and the seasonal trends. Among the different air pollutants, the most significant and widespread reduction is observed for CO (-24 to -67 %), whereas the reduction in the concentration of NO x (-53 to -90 %) and SO 2 (-58 to -84 %) is also apparent in some of the stations ( Fig. 5 ). Since the air quality in India has been arguably improving consistently since 2018 ( Narain et al., 2020 ), the improvements in air quality in 2020 could have also been a continuation of the trend. Kerimray et al. (2020) and Zangari et al. (2020) discussed the significance of this synergism on the improvement of air quality. As a less industrialized and less urbanized state of India, Table S2 shows a comparison of changes in the air quality of Kerala during the lockdown with various studies from more urbanized and industrialized areas worldwide.

The lockdown over the Yangtze River Delta Region (China) lowered NO x , SO 2 , and PM 2.5 emissions by approximately 29–47 %, 16–26 %, and 27–46 % during the Level I and Level II response periods, respectively ( Li et al., 2020 ). In the urban areas of São Paulo (Brazil), the partial lockdown witnessed a drastic reduction of NO (up to -77.3 %), NO 2 (up to -54.3 %), and CO (up to -64.8 %) concentrations compared to the five-year monthly mean. Otmani et al. (2020) reported significant reduction of NO 2 (-96 %), SO 2 (-49 %) and PM 10 (-75 %) concentrations in Sale city (Morocco). The urban areas of Europe also evinced reduced levels of air pollutants ( Sicard et al., 2020 ): NO, NO 2 , PM 2.5 and PM 10 show a reduction by 56–82%, 29–70%, -8 (i.e., increased by 8 %) to 13 %, and -1 (i.e., increased by 1 %) to 40 %, respectively. Chin et al. (2020) observed 40–93 % reduction in the concentration of SO 2 , CO, NO 2 and particulate matter at Denver, Colorado, USA during the “stay at home” period compared to the same period in 2018 and 2019. In the Indian context, Sharma et al. (2020) observed an overall decrease of NO 2 (-18 %), CO (-10 %), PM 2.5 (-43 %) and PM 10 (-31 %) compared to the previous years. Significant reduction in the air pollutant levels is also reported in Gujarat State (India): NO 2 (-30 to -84 %), CO (-3 to -55 %), SO 2 (-22 to -58 %), PM 2.5 (-38 to -78 %) and PM 10 (-32 to -80 %) ( Selvam et al., 2020 ). Mahato et al. (2020) noted that NO 2 , CO, SO 2 , PM 2.5 and PM 10 levels were reduced by 52.68 %, 30.35 %, 17.97 %, 53.11 %, and 51.85 %, respectively compared to pre-lockdown period. The reduction in the particulate matter during the lockdown was also significant compared to the same period of 2017–2019 (i.e., -34.19 % for PM 2.5 and -58.12 % for PM 10 ).

The reduction of the air pollutant levels in the lockdown period significantly varies among the urban, industrial, semi-urban and rural regions. Kanniah et al. (2020) observed that the reduction of the air pollutant levels varies among the industrial (NO 2 = -33 to -46 %; PM 2.5 = -19 to -42 %; PM 10 = -28 to -39 %), urban (NO 2 = -63 to -64 %; CO = -25 to -32 %; PM 2.5 = -23 to -32 %; PM 10 = -26 to -31 %), semi-urban (NO 2 = -55 to -56 %; CO = -25 to -27 %; PM 2.5 = -15 to -28 %; PM 10 = -22 to -27 %) and rural regions (NO 2 = -26 to -34 %; CO = -6 to -7 %; PM 2.5 = -4 to -27 %; PM 10 = -10 to -24 %). Wang et al. (2020) also reported that NO 2 , CO, PM 2.5 and PM 10 concentrations of Hangzhou city during lockdown were reduced (compared to the same period of 2019) by 58.4 %, 22.3 %, 42.7 % and 47.9 %, respectively in urban areas, whereas it was 48.0 %, 20.8 %, 18.5 % and 39.6 %, respectively in rural areas. In general, comparison of the reduction of the air pollutant levels in Kerala State during the LD 2020 with these studies suggests a lesser degree of reduction of the gaseous pollutants (e.g., NO and NO 2 ) and the particulate matter in the State, compared to the highly urbanized and industrialized regions. Although the reduction in the gaseous air pollutant concentrations of Kerala State is comparable with that of the suburban and rural areas ( Kanniah et al., 2020 ; Wang et al., 2020 ), reduction of the particulate matter shows considerable variability among the regions.

Contrary to the general reductions in the air pollutant levels across Kerala, a few stations exhibit a substantial increase in the concentration of NH 3 (5–100 % at K1, K2 and K4), SO 2 (36–85 % at K1 and K3), O 3 (3 % at K1 and 224 % at K4), PM 2.5 (> 50 % at K3 and K4) and PM 10 (40 % at K4) during the LD 2020 ( Fig. 5 ). The major source of NH 3 emissions is agriculture, while minor sources include industrial processes, vehicular emissions and volatilization from soils and oceans ( Behera et al., 2013 ). Hence, the increase in the concentration of NH 3 in Kerala State during the LD 2020 indicates the contributions from the sources other than industrial and transportation sectors. On the other hand, the major urbanized/industrialized areas of India (e.g., Kolkata, Delhi, Chennai) observed a considerable reduction in NH 3 concentration due to the shutdown of transportation sector ( Bedi et al., 2020 ; Mahato et al., 2020 ). One may also note that K3 showed a significant reduction of NH 3 levels (-67 %) during the LD 2020, which could be attributed to the cessation of the industrial activities. The SO 2 levels at K1 and K3 showed a remarkable increase during the LD 2020 ( Fig. 5 a, c). Although the lockdown exerted cessation of the industrial operations, the relative increase in SO 2 levels could be due to the high background/residual pollution and/or due to regional factors (e.g., Kerimray et al., 2020 ; Wang et al., 2020 ). Shehzad et al. (2020) observed a faint trail of NO 2 emission along the major maritime routes of the Indian Ocean, implying the presence of active marine traffic during the period. Hence, the onshore transport of SO 2 due to marine traffic may also influence the concentration of SO 2 of Kerala State.

As a departure from the general behaviour of O 3 across Kerala State, K1 and K4 recorded an increase in O 3 concentration during the LD 2020. Similar observations were reported by various researchers (e.g., Mahato et al., 2020 ; Sicard et al., 2020 ; Siciliano et al., 2020 ; Tobías et al., 2020 ) and attributed to the decrease of NO x concentration along with the increase in the reactivity of the volatile organic compounds (VOC) mixture, the decrease of NO and corresponding reduction of the O 3 consumption (i.e., titration) and the higher rate of insolation and increased temperatures ( Tobías et al., 2020 ). In general, the bivariate relationship between NO x and O 3 at K4 is negatively correlated (Fig. S3) implying that the increase in O 3 levels (224 %) at K4 could be associated with the decrease in the NO x concentration (i.e., -68 %), while K1 and K3 show hardly any definite relationships. Although the general pattern of PM 2.5 and PM 10 can presumably reflect the reduction of transportation and industrial activities, K3 and K4 recorded a significant increase of particulate matter during the LD 2020 ( Fig. 5 c, d). The anomaly at K3 and K4 could relate to various reasons, such as emissions from the sources other than transportation and industrial sectors (e.g., domestic/residential sectors, burning of biomass, log-range transport, etc.) and a higher degree of background/residual pollution, and the contributions from these sources might have offset the reduction of the particulate matter due to the lockdown ( Li et al., 2020 ; Otmani et al., 2020 ; Sicard et al., 2020 ). The concentration of the particulate matter would have significant implications in the quality of life of Kerala State as a reduction in PM 2.5 concentration by 10 % would save 15,904 life years ( Tobollik et al., 2015 ). However, the majority of premature deaths in the Indian context occur in the less-urbanized/rural areas as compared to the heavily urbanized/industrialized regions, where the major factors are PM 2.5 and O 3 from the domestic, agricultural and residential sectors ( Karambelas et al., 2018 ). Therefore, the results of this study have extended implications in the context of health risks associated with air pollution of Kerala State.

Since the transboundary transport is postulated for the increased levels of air pollutants of Kerala State, the role of long-range transport on the air quality was investigated by analysing the air mass trajectories for the LD 2020 by HYbrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) transport model ( Rolph et al., 2017 ; Stein et al., 2015 ). The back-trajectory analysis at K3 and K4 indicates movement of air mass mostly from the Arabian Sea and the eastern parts of peninsular India ( Fig. 6 ). Contrastingly, K1 receives air parcels from multiple regions including the Arabian Sea, the Bay of Bengal, the Indian Ocean and the northern parts of Sri Lanka. However, the seasonal effects on the transport of the pollutants are not addressed in this study. Although the effect of lockdown in Kerala State is manifested as the reduction in the concentration of most of the pollutants, the long-range transport of the pollutants may act as a limiting factor for further improvement of air quality.

Air mass trajectories reached at the air quality monitoring stations (K1, K3 and K4) of Kerala during 1–30 April 2020. The hourly endpoints indicate back trajectories for 24 -h period.

Results of this study help understand the differences in the response of the ambient air quality to short-term human interventions in less industrialized and less urbanized regions to recognize, monitor, and prioritize potential public health concerns and opportunities for action, which are beneficial to develop appropriate policy measures considering reductions in the concentration of the primary and secondary pollutants, as well as the background/residual pollution levels. Although the unprecedented restrictions on the transportation, construction and industrial sectors caused serious negative effects on the economy, the lockdown offered an incomparable opportunity to investigate the role of emissions from various sectors controlling the ambient air quality. The improvement of the ambient air quality due to the lockdown measures seems to help develop better ambient air quality management programmes, especially in the urbanized/industrialized areas, but has differing implications in the less-industrialized/less-urbanized regions.

5.1. Limitations of study

This study addressed the effect of lockdown on the ambient air quality of Kerala (India) considering the temporal variability and trends in the air quality data. This study has three major limitations. The first limitation is a focus of the analysis on the major pollutants, such as NO, NO 2 , NO x , NH 3 , SO 2 , CO, O 3 , PM 2.5 , PM 10 , but all the stations (except K3) did not have the comprehensive data of the pollutants. The second limitation is the lack of long-term air quality data at the monitoring stations. We analysed the trends and estimated the averages of the pollutants based on the data of the previous two years (2018 and 2019). If data were available for a quite long period, however, the estimates of the average concentration of the pollutants, as well as the seasonal trends would have been more reliable. Thirdly, unavailability of meteorological data at the ambient air quality stations limited the present study to understand the role of local meteorological variables on the ambient air quality. We analysed rainfall, minimum and maximum temperature, and relative humidity of January to May (2018–2020) recorded by India Meteorological Department at Thiruvananthapuram, however, indicates hardly any significant differences in the meteorological conditions between 2020 and the previous years.

6. Summary and conclusions

In summary, examination of air pollutants in Kerala State between the pre-lockdown and lockdown periods, seasonal trends in air pollutants, along with comparison with data from more industrialized/urbanized areas, furnished the following answers to the research questions posed in this study. First, the levels of different air pollutants showed a significant reduction during the lockdown period compared to the period before lockdown. The sizeable reductions in concentrations of the pollutants, including NO 2 (-48 %) NO x (-53 % to -90 %), CO (-24 % to -67 %) as well as the particulate matter (-24 % to -47 % for PM 2.5 , -17 % to -20 % for PM 10 ), correlates with decreased emissions from transportation and industrial sources during the lockdown period. Second, a significant decreasing trend in air pollutant levels of Kerala State from January to May over three years (2018, 2019 and 2020) does imply seasonal fluctuations as a cause of the improved air quality. In 2020 affected by the lockdown measures, however, the general decreasing trend in air pollutants was intensified (manifested as the change in Mann Kendall S), or, the positive trend was reversed. Third, the effect of lockdown on the air quality of Kerala State differs from that reported for highly urbanized and industrialized regions, as well as the typical rural regions. Although the reduction of the concentration of the gaseous pollutants of Kerala is comparable with highly urbanized and industrialized regions, the reduction of particulate matter shows considerable variability among the regions. In general, the effects of lockdown were more variable and focused, in that the reduction in the concentration of air pollutants was jointly controlled by lockdown measures as well as seasonal effects. Long-range transport of pollutants originating from the Arabian Sea, as well as higher background and residual pollution, also accounted for the variability.

In conclusion, details of improvements in the ambient air quality of Kerala State can help to understand interactions between short-term human interventions and environmental quality during a global pandemic. Findings from this study provide an example from a less urbanized and less industrialized region in how to recognize, monitor, and prioritize potential public health concerns and opportunities for action, which may be beneficial for developing appropriate policy measures. Although the improvement in the ambient air quality due to the COVID-19 pandemic has been considered an experiment for developing better air quality management programmes across more polluted/urbanized/industrialized regions, this study affirms that such management measures of air quality could have divergent implications in less industrialized and less urbanized regions as well.

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors thank the Kerala State Pollution Control Board, Government of Kerala (specifically Er. Bindhu Radhakrishnan, Senior Environmental Engineer) for providing the ambient air quality data of the air quality monitoring stations of Kerala. The authors are grateful to the India Meteorological Department for providing the meteorological data.

Appendix A Supplementary material related to this article can be found, in the online version, at doi: https://doi.org/10.1016/j.ancene.2020.100270 Lal et al., 2020b , Nakada and Urban, 2020 , Rodríguez-Urrego and Rodríguez-Urrego, 2020

Appendix A. Supplementary data

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Burden of Outdoor Air Pollution in Kerala, India—A First Health Risk Assessment at State Level

Affiliations.

• 1 Department of Environment and Health, School of Public Health, Bielefeld University, Bielefeld, Universitätsstraße 25, Bielefeld 33615, Germany. [email protected].
• 2 Federal Environment Agency, Section Exposure Assessment and Environmental Health Indicators, Corrensplatz 1, Berlin 14195, Germany. [email protected].
• 3 Department of Epidemiology and International Public Health, School of Public Health, Bielefeld University, Bielefeld, Universitätsstraße 25, Bielefeld 33615, Germany. [email protected].
• 4 Federal Environment Agency, Section Exposure Assessment and Environmental Health Indicators, Corrensplatz 1, Berlin 14195, Germany. [email protected].
• 5 Federal Environment Agency, Section Exposure Assessment and Environmental Health Indicators, Corrensplatz 1, Berlin 14195, Germany. [email protected].
• PMID: 26343701
• PMCID: PMC4586631
• DOI: 10.3390/ijerph120910602

Ambient air pollution causes a considerable disease burden, particularly in South Asia. The objective of the study is to test the feasibility of applying the environmental burden of disease method at state level in India and to quantify a first set of disease burden estimates due to ambient air pollution in Kerala. Particulate Matter (PM) was used as an indicator for ambient air pollution. The disease burden was quantified in Years of Life Lost (YLL) for the population (30 + years) living in urban areas of Kerala. Scenario analyses were performed to account for uncertainties in the input parameters. 6108 (confidence interval (95% CI): 4150-7791) of 81,636 total natural deaths can be attributed to PM, resulting in 96,359 (95% CI: 65,479-122,917) YLLs due to premature mortality (base case scenario, average for 2008-2011). Depending on the underlying assumptions the results vary between 69,582 and 377,195 YLLs. Around half of the total burden is related to cardiovascular deaths. Scenario analyses show that a decrease of 10% in PM concentrations would save 15,904 (95% CI: 11,090-19,806) life years. The results can be used to raise awareness about air quality standards at a local level and to support decision-making processes aiming at cleaner and healthier environments.

Keywords: Air pollution; India; Kerala; Years of Life Lost (YLL); environmental burden of disease; particulate matter.

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Journal of Industrial Pollution Control

ISSN (0970-2083)

AN ANALYSIS OF AIR POLLUTION IN KERALA

Amogh P. Kumar 1 , Mohandas K 2 , kshama AV 1 * , Paul lazarus T 3 , Salma muslim 1 and Santha AM 4

1 M.Sc. (Agricultural Economics) students, Department of Agricultural Economics, College of Agriculture, Vellayani, Kerala Agricultural University, Kerala, India.

2 PhD (Agricultural Economics) Student, Department of Agricultural Economics, College of Agriculture, Vellayani, Kerala Agricultural University, Kerala, India.

3 Assistant Professor (SS), Department of Agricultural Economics, College of Agriculture, Vellayani, Kerala Agricultural University, Kerala, India.

4 Associate Professor and Head, Department of Agricultural Economics, College of Agriculture, Vellayani, Kerala Agricultural University, Kerala, India.

Received date: 02 March, 2017; Accepted date: 23 May, 2018

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Air pollution index is an overall scheme that transforms the weighted values of individual air pollution related parameters into a single number. In the Indian context, most commonly used air pollution index (API) is a four parameter model. The longer and more intense the exposure of people to air pollutants such as particulate matter (PM), nitrogen oxides, carbon monoxide, sulphur dioxide etc., the greater the negative impact on their health. The effects range from minor eye irritation, respiratory symptoms, to decreased lung and heart function, hospitalization and even premature death. Hence this study was conducted to study the quality of air in all the districts of Kerala for a period of nine years during the period 2008-2016.

Air Pollution Index, Kerala.

Introduction

Air is an important and vital component of earth’s environment and slight change in its composition can have varied effects on growth and development of organisms on this planet. Air pollutants released from various sources exert detrimental effects on vegetation. The major reason for air pollution is industrial emissions including automobile emissions. Air pollutants have a lot of adverse effects on various platforms. So it is very much important to monitor the air quality status of an area to know whether it is polluted or not. According to source apportionment studies conducted by the Central Pollution Control Board (CPCB, 2010), in cities such as Delhi, Kanpur, Bangalore, Pune, Chennai and Mumbai show that transport sector contributes to more than 70% of the ambient air pollution.

The scientific evidence about the health effects of air pollution is compelling. The longer and more intense the exposure of people to air pollutants such as particulate matter (PM), nitrogen oxides, carbon monoxide, sulphur dioxide etc., the greater the negative impact on their health. The effects range from minor eye irritation, respiratory symptoms, to decreased lung and heart function, hospitalization and even premature death. Hence this study was conducted to study the quality of air in all the districts of Kerala for a period of nine years during the period 2008-2016 (Biju and Vijayan, 2014; Bindu, 2008).

Materials and Methods

All the 14 districts of Kerala were selected for the study. With in each district, few locations were chosen for a period of nine years from 2008 to 2016 and they are as follows Thiruvananthapuram (4 locations), Ernakulam (7 locations), Kollam, Alappuzha, Kottayam, Kozhikode, Kannur and Kasargode (2 locations each), Pathanamthitta, Idukki, Thrissur, Palakkad, Malappuram and Wayanad (1 location each). The monthly and yearly averages of different pollutants such as SO 2 , NO 2 , SPM and RSPM were studied.

Air Pollution Index (API)

Air pollution index is an overall scheme that transforms the weighted values of individual air pollution related parameters into a single number. In the Indian context, most commonly used air pollution index (API) is a four parameter model as, shown below.

where SO 2 , NO 2 , SPM (suspended particulate matter) and RSPM (respirable suspended particulate matter) are measured values, SSO 2 , SNO 2 , SSPM and SRSPM are standard values as per the National Air Quality Standard, 2009. The range of air quality index and its interpretations are given in the Table 1 below.

Table 1: Range of air quality index and its interpretation

Fine particles (PM 2.5 ) pose greatest health risk. These fine particles can get deep into lungs and some may even get into the bloodstream, Exposure to these particles can affect a person’s lungs and heart. Coarse particles (PM 10-2.5 ) are of less concern, although they can irritate a person’s eyes, nose and throat (United States Environmental Protection Agency, 2018). Hence Central Pollution Control Board (CPCB) eliminated SPM in ambient air from the standard in November 2009 (National Ambient Air Quality Status and Trends in India-2010). (Kerala State Pollution Control Board, 2010) (KSPCB) is a subsidiary of CPCB and this instruction has been followed. Subsequently in all the Water and Air Quality Directories published by the KSPCB after 2010 have not included data on SPM (Cropper, et al ., 1997; Dcruz, et al ., 2017; Khan and Ghouri, 2011; Waseem, et al ., 2013).

However, the formula for calculating API remains unchanged and the resultant API values tend to be smaller by ignoring SPM values from the calculation of API. The entire data for this study were obtained from various issues of Water and Air Quality Directory published by KSPCB.

Results and Discussion

The annual average of SO 2 , NO 2 , RSPM and SPM from 14 districts of Kerala have been shown in ( Fig.1 ). The value of highest SO 2 was observed during the year 2008 (4.24 μg/m 3 ) and the lowest during the year 2015 (2.89 μg/m 3 ). The value of NO 2 was highest during the year 2016 (15.86 μg/m 3 ) and the lowest during 2013 (9.71 μg/m 3 ), average RSPM level was highest during the year 2013 with a value of 45.58 μg/m 3 and the lowest during 2011 (38.4 μg/m 3 ).

Fig 1: Air pollutants in Kerala during 2008-2016.

The values of SPM were available only for three years during 2008-10. We can see that SPM values were highest during the year 2008 with a value of 79.21 μg/m 3 followed by 2009 (76.17 μg/m 3 ) and least in the year 2010 with a value of 64.03 μg/m 3 . There is a gradual decrease in SPM in the ambient air during this period.

( Fig. 2 ) shows the Air Pollution Index for three years with SPM. The figure shows that during the year 2008 API value was highest ie.,70.89 followed by the year 2009 with a value of 68.18 and least in the year 2010 with a value of 59.34. The values of API fall within the range of 50-75 during three years. Hence we can say that in Kerala as a whole, moderate air pollution existed during the years 2008-10. Moreover, the quality of air has improved during the period under consideration. API values calculated by including SPM are higher than those calculated by excluding SPM.

Fig 2: Air pollution index including SPM during 2008-2010.

Air pollution index excluding SPM during 2011- 2016 has been shown in ( Fig. 3 ). The API was highest during the year 2016 (38.41) followed by 2012 (36.28), 2013 (36.16), 2014 (36.07), 2015 (35.64) and least in the year 2011 (32.67). The values of API fall within the range of 25-50 during the six years. Hence we can say that all the districts of Kerala had light air pollution during 2011-16.

Fig 3: Air pollution index excluding SPM during 2011-2016.

From Tables 2 and 3 , we can classify the districts in Kerala on the API both by including and by excluding SPM into four ranges of air pollution index. Idukki has least API both with and without SPM among all districts of Kerala.

Table 2: Average API values including SPM in different districts of Kerala during 2008-2010

Table 3: Average API values excluding SPM in different districts of Kerala during 2011-2016

Table 4 shows that four districts viz., Thiruvanathapuram, Alappuzha, Palakkad and Kasargode had experienced heavy air pollution which needs to be addressed as it is affecting the quality of ambient air. Seven districts fall under moderate air pollution category which is indicating the need for measures to control which otherwise would fall under heavy air pollution category.

Table 4: API including SPM during 2008-2010

Table 5 shows that eleven districts fall under the range of light air pollution and two districts under moderate air pollution. Idukki and Patanamthitta districts had clean air.

Table 5: API excluding SPM during 2011-2016

( Fig. 4 ). shows the SO 2 , NO 2 , RSPM and SPM levels over the different months in an year in 14 districts of Kerala. The SO 2 is at its peak in the month of October (5.09 μg/m 3 ) and falls to 3.11 μg/m 3 in July. NO 2 levels reach a peak during the month of July (18.89 μg/m 3 ) and drastically reach the lowest in the next month of August (10.8 μg/m 3 ). The RSPM values are high during the initial two months of the year (January and February with values of 52.58 and 51.16 μg/m 3 respectively) and it falls to 35.72 μg/m 3 in the month of August. Level of SPM starts with a value of 81.98 and 80.94 μg/m 3 during January and February respectively and falls to the lowest value of 62.26 μg/ m 3 in July. Similar trend is observed in both RSPM and SPM values during the year.

Fig 4: Average values of pollutants from all districts during different months.

( Fig. 5 ). shows the average Air Pollution Index over different months in an year. The maximum API is observed in the month of January (76.41) followed by February (74.83) and the least during the month of August (56.58). Majority values of API fall within the range 50-75 but only in the month January it falls within the range of 75-100. Hence we can conclude that during February to December there was moderate air pollution whereas the month of January witnessed heavy air pollution.

Fig 5: API from all districts during different months.

Air pollution index is an important measure to check the quality of the air that surrounds the environment within which we live. The analysis carried out suggests that when the air is mixed with suspended particulate matter the quality of air decreases. The district of Thiruvananthapuram, the capital city of Kerala, experienced heavy air pollution which attracts attention of the government, NGOs, private organizations and the public to reduce the pollution of air by planting more trees, more usage of public transport than the private vehicles, pollution test for all the vehicles periodically, setting up of new standards for the control of air pollution, creating awareness among the people by organizing street plays or through programmes or campaigns etc. so that the people will understand the ill effects of low quality air and come forward to reduce the pollution and thereby improve the quality of air.

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• Published: 05 July 2024

Recent PM 2.5 air quality improvements in India benefited from meteorological variation

• Yuanyu Xie   ORCID: orcid.org/0000-0001-5966-0482 1 ,
• Mi Zhou   ORCID: orcid.org/0000-0001-8600-1503 1 ,
• Kieran M. R. Hunt 2 , 3 &
• Denise L. Mauzerall   ORCID: orcid.org/0000-0003-3479-1798 1 , 4  

Nature Sustainability ( 2024 ) Cite this article

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• Atmospheric chemistry
• Environmental monitoring

Improving air quality amid rapid industrialization and population growth is a huge challenge for India. To tackle this challenge, the Indian government implemented the National Clean Air Programme (NCAP) to reduce ambient concentrations of particulate matter with diameters less than 2.5 μm (PM 2.5 ) and 10 μm (PM 10 ) in hundreds of non-attainment cities that failed to meet the national ambient air quality standards. Here we evaluate the efficacy of the NCAP using data from the national air quality monitoring network combined with regional model simulations. Our results show an 8.8% yr −1 decrease in annual PM 2.5 pollution in the six non-attainment cities with continuous air pollution monitoring since 2017. Four of these six cities achieved over 20% reductions in PM 2.5 pollution by 2022 relative to 2017, thereby meeting the NCAP target. However, we find that ∼ 30% of the annual PM 2.5 air quality improvements, and approximately half of the reductions during the heavily polluted winter months, can be attributed to favourable meteorological conditions that are unlikely to persist as the climate warms. Meanwhile, in 2022, annual PM 2.5 levels in 44 out of 57 non-attainment cities with continuous monitors still failed to meet air quality standards. This work highlights the need for substantial additional mitigation measures beyond current NCAP policies to improve air quality in India.

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Data availability.

Surface PM 2.5 and other air pollution data from the CAAQM network are available at https://airquality.cpcb.gov.in/ccr/#/caaqm-dashboard-all/caaqm-landing . Surface PM 2.5 data from the US AirNow network are available at https://www.airnow.gov/international/us-embassies-and-consulates/ . Manual monitoring data for PM 2.5 and other air pollution data are available at https://cpcb.nic.in/manual-monitoring/ . The CEDS emission database is available via GitHub at https://github.com/JGCRI/CEDS/ . The EDGAR emission database is available at https://edgar.jrc.ec.europa.eu/dataset_ap61 . The ECLIPSE emission database is available at https://iiasa.ac.at/models-tools-data/global-emission-fields-of-air-pollutants-and-ghgs . Satellite observations of SO 2 and NO 2 concentrations from OMI are available at https://giovanni.gsfc.nasa.gov/giovanni/ and from TROPOMI at https://www.temis.nl/airpollution/no2.php . Satellite observations of NH 3 concentrations are available at https://iasi.aeris-data.fr/nh3/ . Meteorology data from ERA5 are available at https://cds.climate.copernicus.eu/cdsapp#!/search?type=dataset and from the NCEI at https://www.ncei.noaa.gov/ . WRF-Chem outputs and processed air quality data generated in this study are publicly available via the Princeton archive at https://doi.org/10.34770/xtje-mj26 .

Code availability

Source code for the WRF-Chem model utilized in this study is available at https://www2.mmm.ucar.edu/wrf/users/download/get_sources.html#WRF-Chem . All custom codes are direct implementation of standard methods and techniques as described in detail in the Methods.

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Acknowledgements

We acknowledge project support from the M.S. Chadha Center for Global India and the Center for Policy Research on Energy and the Environment in the School of Public and International Affairs at Princeton University. K.M.R.H. is supported by a NERC Independent Research Fellowship (MITRE; grant number NE/W007924/1). We acknowledge the Central and State Pollution Control Boards for making surface PM 2.5 pollution measurements available through the CAAQM and NAMP monitoring network. We thank D. Sharma and C. Nguyen for helping to collect the CAAQM surface air quality data for 2015–2022. We thank S. Smith for instructions on using the CEDS global emissions inventory, and C. Venkataraman and T. Ganguly for instructions on using the India national emissions inventories. We also thank F. Paulot, V. Naik, L.W. Horowitz and M. Lin for their suggestions early in this study. We thank M. Nambiar, R. Gupta, E. T. Downie, D. Chug, R. Chandra and W. Dong for constructive feedback on the manuscript.

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Center for Policy Research on Energy and Environment, School of Public and International Affairs, Princeton University, Princeton, NJ, USA

Yuanyu Xie, Mi Zhou & Denise L. Mauzerall

Department of Meteorology, University of Reading, Reading, UK

Kieran M. R. Hunt

National Centre for Atmospheric Sciences, University of Reading, Reading, UK

Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ, USA

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Contributions

Y.X. and D.L.M conceptualized the study. Y.X. retrieved and constructed the dataset and performed the analysis. M.Z. contributed to data processing, WRF-Chem model simulations and model evaluations. K.M.R.H. analysed the western disturbances. Y.X. and D.L.M. integrated the results and wrote the manuscript. All authors contributed to the interpretation of the findings, provided revisions to the manuscript and approved the final manuscript.

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Correspondence to Yuanyu Xie or Denise L. Mauzerall .

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Extended data

Extended data fig. 1 continuous pm monitoring stations in india..

( a ) Location of continuous PM monitoring stations from CAAQM (black) and the US AirNow (green) networks; ( b ) Comparison of daily mean PM 2.5 concentrations measured during 2017–2022 at five US AirNow monitoring sites and all CAAQM sites located within 5 km radius of the US AirNow sites. The correlation r 2 , normalized mean bias (NMB) and number (N) of PM 2.5 measurements are shown.

Extended Data Fig. 2 Continuous PM monitoring for each season.

Changes in the total number of NCAP non-attainment cities that had continuous PM monitoring from the CAAQM and US AirNow networks (bars, left axis) and number of total surface PM monitoring stations the CAAQM and US AirNow networks (lines, right axis) during 2017–2022 for ( a ) spring (MAM), ( b ) summer (JJA), ( c ) fall (SON) and ( d ) winter (DJF).

Extended Data Fig. 3 Continuous PM 10 monitoring stations in India.

( a ) Location of the 131 non-attainment cities (dots) and other cities with PM 10 monitoring (open blue circles) on the topographic map (in meters) over India. Blue indicates where continuous PM 10 monitoring is available from the CAAQM/US AirNow networks for at least one year during 2017–2022; black indicates no continuous PM10 monitoring is available from the CAAQM/US AirNow networks during 2017–2022; ( b ) Time series of annual mean PM 10 concentrations in 2017–2022 averaged in non-attainment cities with consecutive PM 10 data starting from each year during 2017–2021 (right axis); the left axis represent the ratio relative to 2017, the NCAP baseline; the number of cities with available consecutive PM 10 observations up to 2022 (numbers in parenthesis) are shown in different shades of grey; larger dots represent greater number of cities included for averaging; error bars denotes ±one standard error of means across available cities (n=13,17,27,36 for 2018–2021 as reported in parenthesis).

Extended Data Fig. 4 Annual and seasonal PM 2.5 pollution trends in Indian cities.

Time series of ( a ) annual and ( b-e ) seasonal (MAM-spring, JJA-summer, SON-fall, DJF-winter) mean PM 2.5 concentrations in 2017–2022 averaged in cities with consecutive PM 2.5 data starting from 2017 (black, number of cities reported in parenthesis), and for cities with consecutive data starting from 2018–2021 (different shades of grey; number of cities reported at the bottom); seasonal trends for non-attainment (all) cities is shown in black (orange); the left axis represent the ratio relative to 2017, the NCAP baseline; data starting from 2018–2021 are scaled to match with the ratio relative to 2017; larger dots represent greater number of cities included for averaging; error bars denotes ±one standard error of means across available cities (n=7,33,53,74,99 reported in panel a; n=15(17 for all cities),35,37,45,53 reported in panel b; n=11(12 for all cities),29,32,43,52 reported in panel c; n=13(14 for all cities),32,41,46,54 reported in panel d; n=28(36 for all cities),36,42,47,555 reported in panel e).

Extended Data Fig. 5 Observed decreases in surface PM 2.5 during 2017–2022 for each season.

Seasonal mean PM 2.5 measured at CAAQM and U.S. AirNow continuous monitoring sites during 2017–2022 for ( a ) March–May (MAM), ( b ) June–August (JJA), ( c ) September–November (SON), and ( d ) December–February (DJF). Note that DJF for 2017 represents December 2017 to February 2018. The larger dots with black circles represent PM 2.5 concentrations at non-attainment cities that are available for six consecutive years. The smaller dots without black circles represent PM 2.5 levels for cities without six consecutive years of data.

Extended Data Fig. 6 Changes in anthropogenic emissions over India from three global emission inventories.

( a-c ) Timeseries of anthropogenic emissions of SO 2 , NO x , NH 3 , OC, BC, PM 2.5 , PM 10 and NMVOC during 2000–2020 relative to 2017 from CEDS (v2021-04-21, left), EDGAR (v6.1, middle) and ECLIPSE (v6b, right). Data from ECLIPSE during 2019–2020 are projections. ( d ) comparison of annual total emissions in 2017 over India (Tg yr −1 ) from the three global emission inventories.

Extended Data Fig. 7 Changes in SO 2 , NO x and NH 3 during 2017–2022 over India.

( a ) Satellite observed total column SO 2 from OMI in India for 2017, 2022 and the difference between 2022 and 2017. ( b - c ) Same as (a) but for total column NO 2 from TROPOMI ( b ) and OMI ( c ) for 2018, 2022 and the difference between 2022 and 2018. ( d ) same as ( a ) but for total column NH 3 from IASI for 2017, 2022 and the difference between 2022 and 2017. Circles are surface observations of SO 2 ( a ), NO x ( b - c ) and NH 3 ( d ) for 2018, 2022 and the difference between 2022 and 2018.

Extended Data Fig. 8 Correlations between surface PM 2.5 and meteorological variables.

Correlation coefficient r between detrended daily surface PM 2.5 and meteorological variables for daily, 3-day, 5-day and 7-day averages in December–February during 2017–2022. From left to right: surface temperature (T2m), precipitation (Precip), relative humidity (RH), boundary layer height (BLH), surface pressure (Pressure), surface wind speed (WS-10m), temperature inversion between 925hPa and 2m (INV925-2m), temperature inversion between 850hPa and 2m (INV850-2m), 850hPa wind speed (WS-850), 500hPa wind speed (WS-500). Dots indicate statistical significance at 95 percentile confidence intervals.

Extended Data Fig. 9 Changes in meteorology in the winter of 2017 and 2021.

Differences in inversion ( a , b ), precipitation ( c , d , contour), wind speed at 10 meters ( e , f ) and transect of geopotential height and vertical-meridional circulation anomalies averaged between 73E–88E ( g , h ) in the winter of 2017 (left) and 2021 (right) relative to 2000–2022 mean. Tracking of western disturbance with average vorticity greater than 9e −5 m/s over northern India are each shown in c , d with different colors and shapes. Black shading in g , h indicates the surface topography along the transects.

Extended Data Fig. 10 Sensitivity simulations with changing emissions for the winter of 2017 and 2021.

( a ) Differences in the simulated surface PM 2.5 concentrations (in percent) in response to changes in emission alone (EMIS), meteorology alone (MET), and to changes in both emission and meteorology (EMIS+MET) relative to the baseline simulation with emissions and meteorology from 2017. Difference for emission perturbation simulations for 2021 (orange and dark blue) is compared to simulation with emissions from 2017 and meteorology from 2021. ( b ) same as ( a ) but for meteorological variables. The observed changes (OBS) in PM 2.5 and meteorological variables are shown as gray bars. Bars represent changes averaged from the 28 non-attainment cities shown in Fig. 4c, d , circles represent changes averaged from all 131 non-attainment cities.

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Xie, Y., Zhou, M., Hunt, K.M.R. et al. Recent PM 2.5 air quality improvements in India benefited from meteorological variation. Nat Sustain (2024). https://doi.org/10.1038/s41893-024-01366-y

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AIR QUALITY STUDY OF SELECTED AREAS IN KERALA STATE

https://www.irjet.net/archives/V4/i4/IRJET-V4I4843.pdf

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Keerthi Lakshmi, K.A.; Nishanth, T.; Satheesh Kumar, M.K.; Valsaraj, K.T. A Comprehensive Review of Surface Ozone Variations in Several Indian Hotspots. Atmosphere 2024 , 15 , 852. https://doi.org/10.3390/atmos15070852

Keerthi Lakshmi KA, Nishanth T, Satheesh Kumar MK, Valsaraj KT. A Comprehensive Review of Surface Ozone Variations in Several Indian Hotspots. Atmosphere . 2024; 15(7):852. https://doi.org/10.3390/atmos15070852

Keerthi Lakshmi, K. A., T. Nishanth, M. K. Satheesh Kumar, and K. T. Valsaraj. 2024. "A Comprehensive Review of Surface Ozone Variations in Several Indian Hotspots" Atmosphere 15, no. 7: 852. https://doi.org/10.3390/atmos15070852

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Center for Policy Research on Energy and the Environment (C-PREE)

Recent Air Quality Improvements in India Partially due to Meteorological Variation

Photo Credit: Abhisheklegit from Getty Images

Rapid industrialization and population growth has led to significant increases in emissions and air pollution to dangerous levels in India.  To address this challenge, the Indian government implemented the National Clean Air Programme (NCAP) to reduce emissions of fine particulate air pollutants (particulate matter with diameters less than 2.5 microns (PM2.5) and 10 microns (PM10) and their precursors) in cities which fail to meet the Indian air quality standards (i.e., non-attainment cities).  Though cities have seen noticeable decreases in fine particulate concentrations since the implementation of the program, a Princeton-led  study finds that a significant portion of the improvements in air quality result from favorable meteorological conditions that are unlikely to persist as the climate changes.  

In 2019, India’s Ministry of Environment, Forest, and Climate Change launched the NCAP to help reduce particulate matter (PM) air pollution by 20-30% by 2024 relative to 2017.  The NCAP provided over 10,400 crores ($1.2 billion) of financial support to help the 131 non-attainment cities expand their pollution monitoring capacity, city action plans, and public awareness campaigns.  However, meteorological variability can change ventilation and the dilution of pollution, thus driving large variations in surface PM2.5 concentrations that make it hard to gauge policy effectiveness.   

“To disentangle the role of meteorology from emission changes, we need high-quality surface measurements and atmospheric chemistry models that can accurately reproduce observed pollutant concentrations,” explains lead author  Yuanyu Xie , an associate research scholar at C-PREE.  “However, very limited studies have carefully examined surface pollution data or provided comprehensive evaluations of model performance.”

In this study, Dr. Yuanyu Xie,  Prof. Denise Mauzerall , and their research team used the  recently expanding national surface continuous PM monitoring data (2017-2022) available from the Indian Central and State Pollution Control Boards (CPCB and SPCB) and ran regional model simulations to evaluate the effectiveness of NCAP policies in non-attainment cities.  The researchers strictly controlled the quality of the collected surface measurements they used and analyzed observed annual, seasonal, and daily mean PM concentrations for comparison with the NCAP’s PM reduction target of 20-30%.  

The results showed an 8.8% per-year decrease of PM2.5 pollution in the six non-attainment cities from 2017 through 2022.  In 2022, four of these cities met the NCAP target and achieved over 20% reductions relative to 2017 levels of PM2.5 pollution.  However, the authors note that although primary PM2.5 emissions appear to be decreasing, emission of key PM2.5 precursors (SO2, NOx, and NH3) have not been decreasing and have even increased in some locations since 2017.  

“Decreasing pollution concentrations are beneficial for public health,” says Xie.  “However, if these improvements are not primarily driven by substantial emission reductions, the current air quality improvements may not be sustainable. Increasing emissions of PM2.5 precursors we found could also indicate that existing policies are either ineffective or have not been fully implemented and enforced.”

Given the small change in anthropogenic emissions, the researchers then investigated the extent to which the decrease in PM2.5 pollution can be attributed to meteorological variability by conducting six years of regional atmospheric chemistry transport model simulations with fixed emissions at 2017 levels.  PM2.5 reductions attributable to meteorological variability were then estimated as the difference between the simulated PM2.5 changes and the observed PM2.5 changes.

The results showed that approximately 30% of PM2.5 air quality improvements, and approximately 50% of the reductions during the heavily polluted winter months, can be attributed to favorable meteorological conditions. However, even with favorable meteorological conditions, in 2022, 44 out of the 57 non-attainment cities with continuous PM monitoring data still failed to meet the national air quality standards. The results highlight the need for significant additional mitigation measures - especially since these favorable meteorological conditions are unlikely to persist as the climate warms.  

“India, along with other countries in the global South, face the dual challenge of rapid industrialization with resulting simultaneous increases in air pollutant and greenhouse gas emissions,” says Denise Mauzerall, a faculty member at Princeton’s School of Public and International Affairs and the School of Engineering and Applied Science.  “Co-benefits for air quality, health and climate will result from on-going efforts in India to move away from coal and towards renewable energy.  Substantial additional mitigation action beyond current air pollution control policies, especially measures that simultaneously mitigate greenhouse gas and air pollutant emissions, such as energy system decarbonization, electrification and reductions in agricultural waste burning, are essential to achieve healthier air quality and contribute to slowing the rate of global climate change.”

The paper, “Recent PM2.5 air quality improvements in India benefited from meteorological variation,” was co-authored by Yuanyu Xie (School of Public and International Affairs, Princeton University), Mi Zhou (School of Public and International Affairs, Princeton University), Kieran M. R. Hunt (Department of Meteorology and the National Centre for Atmospheric Sciences, University of Reading), and Denise Mauzerall (School of Public and International Affairs and the Department of Civil and Environmental Engineering, Princeton University). The paper appeared in Nature Sustainability on May 6th, 2024.  

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Noida spent only 6% of NCAP funds for air pollution control, shows data

Launched in 2019, the ncap is india's first national effort to set clean air targets, aiming for a 20-30 per cent reduction in pm10 pollution by 2024, with 2017 as the base year.

The 82 cities utilised Rs 831.42 crore of the total Rs 1,616.47 crore allocated, while the 49 cities receiving funding under the 15th Finance Commission spent Rs 5,974.73 crore of the total Rs 8,951 crore allocated. Photo: Bloomberg

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First Published: Jul 21 2024 | 4:58 PM IST

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