key: cord-284583-urh0xk7r
authors: Singh, Vikas; Singh, Shweta; Biswal, Akash
title: Exceedances and trends of particulate matter (PM2.5) in five Indian megacities
date: 2020-08-11
journal: Sci Total Environ
DOI: 10.1016/j.scitotenv.2020.141461
sha: 
doc_id: 284583
cord_uid: urh0xk7r

Abstract Fine particulate matter (PM2.5) is the leading environmental risk factor that requires regular monitoring and analysis for effective air quality management. This work presents the variability, trend, and exceedance analysis of PM2.5 measured at US Embassy and Consulate in five Indian megacities (Chennai, Kolkata, Hyderabad, Mumbai, and New Delhi) for six years (2014–2019). Among all cities, Delhi is found to be the most polluted city followed by Kolkata, Mumbai, Hyderabad, and Chennai. The trend analysis for six years for five megacities suggests a statistically significant decreasing trend ranging from 1.5 to 4.19 μg/m3 (2%–8%) per year. Distinct diurnal, seasonal, and monthly variations are observed in the five cities due to the different site locations and local meteorology. All cities show the highest and lowest concentrations in the winter and monsoon months respectively except for Chennai which observed the lowest levels in April. All the cities consistently show morning peaks (~08: 00–10:00 h) and the lowest level in late afternoon hours (~15:00–16:00 h). We found that the PM2.5 levels in the cities exceed WHO standards and Indian NAAQS for 50% and 33% of days in a year except for Chennai. Delhi is found to have more than 200 days of exceedances in a year and experiences an average 15 number of episodes per year when the level exceeds the Indian NAAQS. The trends in the exceedance with a varying threshold (20–380 μg/m3) suggest that not only is the annual mean PM2.5 decreasing in Delhi but also the number of exceedances is decreasing. This decrease can be attributed to the recent policies and regulations implemented in Delhi and other cities for the abatement of air pollution. However, stricter compliance of the National Clean Air Program (NCAP) policies can further accelerate the reduction of the pollution levels.

Air quality in megacities is a major concern for human health where a large portion of the population lives, and the pollution levels often exceed the limit values (Kumar et al., 2015; Zheng et al., 2017) . Among all pollutants, PM 2.5 (particles less than 2.5 micrometers in diameter) poses a greater risk as it can penetrate deep into the human body (Xing et al., 2016) . It has been estimated that exposure to outdoor PM 2.5 is the fifth leading risk factor worldwide and the third leading risk factor in India (GBD 2015 Risk Factors Collaborators, 2016 . Globally, exposure to PM 2.5 accounts for 4.2 million deaths and over 100 million disability-adjusted life-years in 2015 (GBD 2015 Risk Factors Collaborators, 2016 . A growing number of epidemiological evidence of acute and chronic impacts of PM 2.5 on human health, besides its role in perturbing weather and climate (Fuzzi et al., 2015) , has led the scientific community to monitor levels of PM widely across urban, suburban and rural regions of different countries in the last decade. However, inter-comparison of these results is not always possible either because of the difference in sampling or monitoring instrument or due to different sampling duration. This requires a sampling network that works on one principle with large spatial coverage.

In recent times, United States Environment Protection Agency (US-EPA) has come up with PM 2.5 monitoring at the U.S. Embassy and Consulates (USEC) in various countries using Federal Equivalent Method (FEM) approved instrument Beta Attenuation Monitor (BAM-MetOne 1020) and is providing hourly measurements of PM 2.5 in 27 countries of the world.

This USEC data has been used for the study of PM 2.5 levels in the urban environment for different purposes viz; to study the trend and characteristics of PM 2.5 ( Chen et al., 2020; Fontes et al., 2017; Sreekanth et al., 2018; Liang et al., 2016; Batterman et al., 2016; San Martini et al., 2015) , to compare with other data and model evaluation (Jiang et al., 2015; Li, Figure 1 . Open street maps of the 2km × 2km area surrounding the embassy/consulates (red circle) to show the geographical location of the five Indian megacities (a. Chennai, b. Kolkata, c. Hyderabad, d. Mumbai, e. New Delhi) .

While meteorology plays an important role in controlling the air quality, the local emission sources mainly household and traffic emissions (Singh et al., 2018a) 

The PM 2.5 concentrations across all the sites are monitored in real-time using FEM BAM-1020, having a standard range of 0-1000 µg m -3 , resolution of ±0.1 µg m -3 and 24 h average lower detection limit less than 1.0 µg m -3 , and the data is processed using a common quality control protocol defined by USEPA (Ray & Vaughn, 2013) . However, we find that there are still negative values with valid flag and outliers (sudden spikes) present in the data set. Therefore, we have further processed a quality control check to remove the outliers. Any data point which is more than three local scaled median absolute deviations (MAD) from the local median of the data within a running window of 6 hours has been considered as an outlier. As 7

The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) hourly surface reanalysis meteorological products from 1 January 2014 to 31 December 2019 has been obtained from NASA's Global Modeling and Assimilation Office (GMAO).

These products are available at a horizontal resolution of 0.5°× 0.625° (https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/). Details of the MERRA-2 products and evaluation has been reported by Gelaro et al., (2017) , Randles et al., (2017) and . The hourly meteorological data (wind, temperature, precipitation, planetary boundary layer height-PBLH) have been extracted from the corresponding grids of the latitude and the longitude of the five megacities (Supplementary Table 2 

The air quality data has been analyzed from 2014 to 2019. We have used Seasonal Trend decomposition procedure based on LOESS (LOcally wEighted Scatterplot Smoothing) smoothing (STL) (Cleveland et al., 1990) to estimate the trend in PM 2.5 as adopted by Bigi and Ghermandi (2016) to study the trend in PM 2.5 in the Po Valley, Italy. STL is a widely used filtering procedure for decomposing time series into three components: trend, seasonal, and remainder or residual. The decomposition is based on a sequence of smoothing procedures using a locally weighted regression known as LOESS (Cleveland et al. 1990 ). The LOESS smoother is based on fitting a weighted polynomial regression for a given time of observation, where weights decrease with distance from the nearest neighbor. Time-series of monthly mean PM 2.5 concentration at all five megacities were decomposed in trend, seasonal, and remainder components using STL procedure (Cleveland et al., 1990) . As the PM 2.5 data is not normally distributed (Supplementary Figure 1) , time-series data were log-transformed before STL decomposition to attain normally distributed residuals and to control heteroscedasticity. Time-series data were back-transformed from logarithmic decomposed data to analyse the trend. A significant slope in the monthly trend component was calculated using Generalized least squares (GLS) regression (Brockwell and Davis, 2002) for each site within a 95% confidence interval (CI) with a significance level (alpha=0.05). GLS is used to J o u r n a l P r e -p r o o f Journal Pre-proof estimate the linear relation between an autocorrelated time series and time to obtain independent residuals and a correct estimate of the variance of the regression coefficients.

STL and GLS analysis were performed using R software.

Exceedance analysis has been performed and the number of threshold exceedances has been calculated by keeping the threshold of daily mean PM 2.5 equal to 60 µg/m 3 and 25 µg/m 3 as per NAAQS (CPCB, 2009) and WHO (WHO, 2005) standards respectively. The linear trend (Singh et al., 2018b) in the annual exceedances in six years has been calculated for each site within 90% confidence interval (CI) with a significance level (alpha=0.1) because of the small sample size (Labovitz, 1968) . We have also calculated the number of pollution episodes and the length of each episode. An episode has been considered when the daily mean PM 2.5 has exceeded continuously for three or more days.

The variation in the PM 2.5 levels for a location is an interplay of emissions, geography, and meteorological conditions (Alimissis et al., 2018; Ganguly et al., 2019; Nair et al., 2007) .

Previously, the diurnal and seasonal variations have been reported for five cities for the period of fewer than four years 2013-2016 by Sreekanth et al. (2018) and for four cities excluding Kolkata for four years (2015-2018) by Chen et al. (2020) . However, these studies did not discuss the trend in the PM 2.5 . Various pollution mitigation schemes along with the public awareness programs (NCAP, MoEFCC, 2019) have been implemented in India since the availability of the USEC data. Therefore, we analysed the data for a longer period of six years for all available USEC sites in India to study the trend, exceedance and variations in details.

The diurnal variation of PM 2.5 for Chennai, Kolkata, Hyderabad, Mumbai, and New Delhi is presented in Figure 2 All the cities consistently show morning peaks around (~08:00-10:00 hrs). A shift of up to two hours in the morning peak hours is due to the season and the geographical location. For a city, the winter peak appears at a later hour of the day than the summer peak hour because of late sunrise and onset of human activities in winter. Among all, the cities located in the eastern side of India (Kolkata, Chennai, and Hyderabad) show a peak around an hour earlier than the cities located further west (Delhi and Mumbai) because of the early sunrise and human activities in the eastern cities. The cities Hyderabad and Chennai show a sharp peak during morning traffic hours, whereas the same is not true for New Delhi, Mumbai, and

Kolkata. The higher levels of PM2.5 during nighttime leads to a smaller peak during morning traffic peak hours. A similar study carried out by Chen et al. (2020) has attributed this to the higher population of Mumbai and New Delhi. This may not be the sole reason as the population of Hyderabad and Chennai are also large enough to enhance the nighttime emissions. The traffic sources in the vicinity of the monitoring station ( Figure 1 ) along with the local meteorology may be responsible for the sharp peak during the morning hours. The morning peak is attributed to the morning fumigation effect after the sunrise (Stull, 2012; Nair et al. 2009 ), along with morning traffic and household emissions (Tiwari et al., 2013b) trapped within the evolving shallow boundary layer. The diurnal peak of PM 2.5 occurs in the morning hours for all the cities except for Kolkata where the peak PM 2.5 is found at midnight.

For Kolkata, a similar variation in BC has been reported by Talukdar et al. (2015) who have also shown the highest peak for BC during midnight rather than morning traffic peak time.

This could be due to the late evening household emissions. Moreover, higher wind prevalence in the day time on account of stronger sea breeze during the early morning to afternoon as compared to the other periods of the day (Gururaja et al., 2019) can also explain the lower concentration in the day time as compared to the night in Kolkata. We also calculated the ratio of the highest to the lowest monthly mean PM 2.5 concentration for a city to know the extent of the variability within a year. As the highest pollution levels are found in winter and lowest in monsoon, it can also be considered as the most polluted to the cleanest ratio. This ratio is found to be high for Kolkata (6.98) and Delhi (6.82) followed by Mumbai (4.9), Hyderabad (3.18), and Chennai (2.98). This suggests that for Kolkata and Delhi, the winter months PM 2.5 levels can be 7 times higher than the monsoon levels.

Journal Pre-proof 

Here we utilize the USEC PM 2.5 data to calculate the annual trend with the monthly mean PM 2.5 across all five Indian cities for six years. Monthly mean PM 2.5 time series at all five locations were decomposed in trend, seasonal and remainder components using STL procedure and the slope in the trend component was calculated using GLS. STL decomposition of the monthly mean PM 2.5 along with GLS fitted models for the five cities are shown in Figure 4 . The equation shown in the figure depicts the GLS linear regression slope with 95% CI and the same has been shown in Table 1 . As can be seen from figure 4, all the cities show a significant decline (negative) trend ranging from 1.5 to 4.19 µg/m 3 per year.

The highest decline trend of 4.19±1.12 µg/m 3 per year was found for New Delhi whereas We have also checked whether the trend in the PM 2.5 is affected by trend in the meteorological parameters such as wind speed, PBLH, and precipitation during the six years.

These meteorological parameters are obtained from MERRA-2 reanalysis and have been validated (Supplementary Table 3 ) against the surface observations at the airports. The trend in the meteorological parameters has been calculated in the same way as it was done for PM 2.5 . The calculated trend in wind speed, temperature, PBLH, and annual precipitation is shown in Supplementary Figure 5 . It is found that wind speed, temperature, PBLH and precipitation do not exhibit a significant change during the study period. Therefore, this analysis confirms that the reduction in PM 2.5 is not due the meteorology but due to the reduction in emissions. Various pollution mitigation schemes along with the public awareness programs (NCAP, MoEFCC, 2019), could have led to the reduction of PM 2.5 levels in Delhi.

J o u r n a l P r e -p r o o f 

Air quality standards, guidelines, objectives, targets, and limit values are defined by the local authorities to control air pollution. The levels below the standard or limit value are (Bran & Srivastava, 2017) for the year 2008 found PM 2.5 mass concentration 2-3 times higher than Indian NAAQS and WHO standards. Moreover, remote sensing based study by Dey et al. (2012) has shown that 51% of the Indian population is exposed to the levels that exceed the WHO annual air quality threshold of 35 μg/m 3 . 

The chemical composition of PM 2.5 offers vital information on the contributions of specific sources and help to understand aerosol properties and processes. PM 2.5 chemical components have been found to vary considerably among different sites across the globe (Snider et al., 2016) . Global population-weighted PM 2.5 concentrations were dominated by particulate organic mass, secondary, mineral dust as well as secondary inorganic aerosols such as sulfates, nitrates and ammonium (Philip et al., 2014) . In addition to the observed trend of PM 2.5 , it is also important to know the variability and trend in the chemical composition. The relation between PM 2.5 exposure and associated health effects is linked with physical and chemical characteristics of the PM 2.5 , and therefore requires attention along with its sources for better management of urban air pollution (Braziewicz et al., 2004; Srimuruganandan and Nagendra, 2011) . However, the unavailability of long-term chemical composition records restricts the detailed analysis of the possible sources. Moreover, one can conduct modelling analysis of PM 2.5 composition but it is considerably challenging because of the combination of uncertainties in the magnitude and spatial and temporal allocation of primary PM 2.5 emissions and our limited understanding of the chemical production pathways for secondary constituents (Mathur et al., 2008 , Appel et al., 2008 were observed to be highest during winter followed by post monsoon>summer>monsoon (Jain et al., 2020; Kota et al., 2018, Sharma and Pant et al., 2015) , SO 4 2− was reported to be most abundant during summer followed by monsoon>post monsoon>winter (Jain et al., 2020; Pant et al., 2015) . Secondary NO 3 − is thermally unstable at higher J o u r n a l P r e -p r o o f Journal Pre-proof temperatures whereas at low temperatures during winter its formation is favorable (Cesari et al., 2018) . Higher photochemical activities during the summer season and high humid conditions during monsoon favors the formation of secondary SO 4 2− (Jain et al., 2020; Goel et al., 2018 , Pant et al., 2015 . While Na + is observed highest during monsoon owing to its sea origin (Jain et al., 2017; Saxena et al., 2017) , Cl − is also significantly linked with wood combustion, open waste burning, coal combustion and industries (Rai et al., 2020; Ali et al., 2019; Pant et al., 2015) , and therefore is observed highest during winter Sharma and Mandal, 2017; Jain et al., 2017; Sharma et al., 2016; ) along with the biomass burning marker ion K + (Sudheer et al., 2014; Tiwari et al., 2013a) . Organic components of PM 2.5 like levoglucosan has been linked with biomass burning in winter (Pant et al., 2015) .

PAHs, linked with biomass burning and road transport, showed the highest concentration during winter, followed by post monsoon> summer>monsoon (Gadi et al., 2019; Singh et al., 2011) . EC and OC which are emitted from vehicular emissions and biomass burning (Ram and Sarin, 2011; Sharma et al., 2016) are reported to be higher during winter than summer (Jain et al., 2017; Sharma and Mandal 2017; Tiwari et al., 2014) . Elemental contribution analysis showed the higher contribution of road dust and soil (marked with higher Si value) during summer whereas for the winter season, the contribution of biomass burning was high (marked with higher K value) Saxena et al., 2017; Jain et al., 2020; Pant et al., 2015) .

For Kolkata, roughly 50% of the PM 2.5 mass was reported to be constituted of ions (Na + , K + , Mg 2+ , Ca 2+ , NH 4 + , SO 4 2− , NO 3 − , Cl − ) and carbonaceous particles (EC, OC) (Chatterjee et al., 2012) . Higher concentrations of NH 4 + , SO 4 2− , and NO 3 − were observed during winter than summer, however, the SO 4 2− oxidation ratio, which is an indicator of secondary SO 4 2− formation, was found to be highest during summer months. Kolkata showed the highest Na + during monsoon, whereas higher Cl − was observed during dry seasons (Chatterjee et al., 2012) and were linked with biomass and coal burning. EC and OC for Kolkata as well showed the highest levels during winter, followed by summer and lowest in monsoon (Chatterjee et al., 2012; Talukdar et al., 2015) . For Mumbai, the major chemical constituents observed were ions (Na + , K + , Ca 2+ , NH 4 + , SO 4 2− , NO 3 − , Cl − ) and carbonaceous particles (EC, OC) and some trace and heavy metals. While Secondary ions (NH 4 + , SO 4 2− , and NO 3 − ), EC and OC were observed higher either during winter or post-monsoon season owing to inland contribution. Non-sea salt sources were of anthropogenic origin (Joseph et al., 2016) .

Elemental analysis showed a significant contribution of the sea during monsoon, and soil dust J o u r n a l P r e -p r o o f Journal Pre-proof during the summer season (Police et al, 2018) . For Hyderabad, the studied chemical constituents were EC and OC (Ali et al., 2016) . While both EC and OC showed the highest winter concentration than during summer, followed by the monsoon, the concentration variation during winter to summer transition for the two carbonaceous fractions is quite different. EC showed a significant decrease during the transition of winter to summer, the same was not true for OC as secondary organic carbon formation and biomass burning added to the total OC levels during summer. Elemental analysis showed the importance of sources like resuspended dust and vehicular emission for the city (Gummeneni et al., 2011) . For

Chennai, the reported chemical constituents of PM 2.5 were ions (Na + , K + , Ca 2+ , Mg 2+ NH 4 + , SO 4 2− , NO 2 − NO 3 − , Cl − , F − ) and some trace and heavy metals (Jose et al., 2019; Srimuruganandam and Nagendra, 2011 showed higher concentration during monsoon, followed by summer>winter (Srimuruganandam and Nagendra, 2011) . Marine aerosols showed a significant contribution for the coastal city (Jose et al., 2019) .

We also performed the five days backward trajectories analysis using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT version 4) model (Stein et al., 2015; Draxler and Rolph, 2003) 

Major sources of primary PM 2.5 in India are emissions from the household, power sector, industries, transport, open burning (crop and waste) and dust (Conibear et al., 2018; Guo et al., 2017; Venkataraman et al., 2018) . Although the household emissions are dominant across India (Apte and Pant, 2019) , vehicular exhaust and dust resuspension (Singh et al., 2020a) remain the dominant local source in Indian cities (Guttikunda et al., 2014 . Other urban sources include construction dust, industrial exhaust, and domestic cooking and heating . Most of the sources of PM 2.5 in urban areas are local, however nonlocal contribution can be significant Guo et al., 2017) . For eg.in Delhi, local sources contribute ~70% of total PM 2.5 , but the non-local sources contribute over 30% especially in winter (Guo et al., 2017) . The emissions neighboring rural areas, contribute to the urban pollution in India Ravindra et al., 2019a; 2019b) . Rural households in India rely on kerosene for light in the absence of electricity, and on wood, J o u r n a l P r e -p r o o f Journal Pre-proof dung, and other solid fuels for cooking and heating. (Chowdhury et al., 2019 , Ravindra et al., 2019c . Use of these fuels emit particles, gaseous pollutants, and volatile organic compounds, and therefore are a significant source of secondary particulate matter in both rural and urban areas (Pervez et al., 2019; Rooney et al., 2019) . In addition to household and traffic emissions, open waste burning is also a significant contributor to the total PM 2.5 in Indian cities Kumari et al., 2019) . The open waste burning is prevalent during winter and over the urban areas with low socioeconomic status (Nagpure et al., 2015) .

Other sources that determine the urban PM 2.5 levels include industries, thermal power plant, brick production, and use of diesel generator sets, however, the influence of these sources is highly variable .

Future emissions scenario studies conducted over India predict an increase in PM 2.5 (Venkataraman et al., 2018; Pommier et al., 2017) , however studies (Chowdhury et al., 2019; Purohit et al., 2019; Venkataraman et al., 2018; Conibear et al., 2018; Bhanarkar et al., 2018) have shown that significant reduction in PM 2.5 is achievable by implementation strict measures to reduce the PM 2.5 emissions. It has been shown by Chowdhury et al., (2019) that a transition from bad fuel to clean fuel in the household has the potential to significantly reduce the PM 2.5 levels at the national level. However, at the urban or city level, where cleaner fuel is used, reduction in vehicular emissions (exhaust and non-exhaust) can bring down the PM 2.5 levels as observed during the COVID lockdown in Indian cities and

significantly reduce the traffic exposure (Singh et al., 2020b) .

We propose that the reduction in PM 2.5 levels across the cities is due to the recent measures taken to reduce the ambient pollution levels in India. The major recent initiatives that might have helped in the reduction include the launch of the National Air Quality Index (AQI) for public awareness, the formation of Environment pollution (prevention and control) authority, implementation of a Graded Response Action Plan (GRAP) and Comprehensive Action Plan LPG is lower than that of solid fuel (Deepthi et al., 2019) , the implementation of PMUY across India would have reduced PM 2.5 levels mainly at the regional level (Chowdhury et al., 2019) . However, people's attitudes towards fuel usage may lessen the expected reduction in emission linked with this switch to cleaner fuel usage as solid fuels are much cheaper and easily available (Ravindra et al., 2019c) . Although waste management is a major challenge (Kumar et al., 2017) , a major step to improve the door-to-door waste collection and disposal as a part of Swachh Bharat Mission (swachhbharatmission.gov.in; Ghosh, 2016) in urban areas could have resulted in the improvement in air quality.

For the reduction of traffic exhaust emissions, the emission standard of the fleet was improved to BS-IV from April 2017 and BS-VI was scheduled from April 2020. The old fleet scrappage program was launched, and electric vehicles are being promoted. Apart from this, shifting to alternate cleaner fuels like CNG, ethanol blending in petrol are some of the steps taken for the cleaner transport sector. Moreover, the use of a modern public transport system was promoted in recent years to reduce traffic emissions. While these measures reduce the exhaust emissions, the maximum reduction is expected in road dust resuspension emission by regular road dust cleaning by mechanized vacuum dust cleaners (Goyal et al., 2019; Gulia et al., 2018) . Other measures include dust control from the building and road construction activities. The stringent measures to limit the crop residue burning. 

This study reports a detailed analysis of the variabilities and trends in the PM 2.5 concentration measured at the US embassy and consulates in the five megacities (Chennai, Kolkata, Analysis of MERRA-2 meteorological parameters suggests no significant change in the annual mean wind speed, temperature, PBLH, and precipitation in the past six years. Despite that, PM 2.5 has been found to exhibit a declining trend.

We have also reported the number of threshold exceedances of daily mean PM 2.5 as per the WHO (25 µg/m 3 ) and Indian NAAQS (60 µg/m 3 ). In addition, the number of pollution episodes and length of each episode (levels above limit values continuously for three or more days) has been reported. We found that the PM 2.5 levels in the cities exceed WHO standards for more than 50% of days in a year with a few exceptional years in Chennai. J o u r n a l P r e -p r o o f So far, we have not come across any study that has suggested a significant decline trend in air quality in Indian cities despite the measures by the local authorities. This is the first study, to the best of our knowledge, that has reported statically significant decreasing trends of PM 2.5 in Indian megacities. This decrease can be attributed to the recent policies and regulations (NCAP, MoEFCC, 2019) implemented in Delhi and other cities for the abatement of air pollution. The implementation of source sector-specific measures related to vehicular emissions, road dust re-suspension and other fugitive emissions, bio-mass/ municipal solid waste (MSW) burning, industrial pollution, construction, and demolition activities, etc. were the major steps towards the mitigation of air pollution. The mitigation measures implemented until June 2018 were expected to deliver an overall decline of ambient PM 2.5 despite economic growth (Purohit et al., 2019) . While a reduction in PM 2.5 is found in Delhi, it continues to be the most polluted city among five megacities. With the annual rate of reduction observed here, it may take another two decades for the pollution levels to come within Indian NAAQS levels. Therefore, stricter compliance of the NCAP policies can further accelerate the reduction of the pollution levels to reduce the health impacts across all

India. The results presented in this study will support the National Clean Air Program of India and help to evaluate the pollution reduction in Indian megacities due to decreased activities post COVID-19 lockdown in India.

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Trend in household energy consumption pattern in India: a case study on the influence of socio-cultural factors for the choice of clean fuel use

Standard Operating Procedure for the Continuous Measurement of Particulate Matter: Thermo Scientific TEOM® 1405-DF Dichotomous Ambient Particulate Monitor with FDMS® Federal Equivalent Method EQPM-0609-182 for

Impacts of household sources on air pollution at village and regional scales in India

Statistical analysis of PM2. 5 observations from diplomatic facilities in China

Planetary boundary layer height over the Indian subcontinent: Variability and controls with respect to monsoon

Water soluble inorganic species of PM10 and PM2. 5 at an urban site of Delhi, India: seasonal variability and sources

Exploring the relationship between surface PM2.5 and meteorology in Northern India

Chemical composition of fine mode particulate matter (PM2. 5) in an urban area of Delhi, India and its source apportionment

Spatio-temporal variation in chemical characteristics of PM 10 over Indo Gangetic Plain of India

Evaluation of air quality model performance for simulating long-range transport and local pollution of PM2. 5 in Japan

Characterization of particulate-bound polycyclic aromatic hydrocarbons and trace metals composition of urban air in Delhi

High resolution vehicular PM10 emissions over megacity Delhi: Relative contributions of exhaust and non-exhaust sources

An approach to predict population exposure to ambient air PM2. 5 concentrations and its dependence on population activity for the megacity London

Estimation of high resolution emissions from road transport sector in a megacity Delhi. Urban climate

Trends of atmospheric black carbon concentration over the United Kingdom

A highresolution emission inventory of air pollutants from primary crop residue burning over Northern India based on VIIRS thermal anomalies

Satellite remote sensing of fine particulate air pollutants over Indian mega cities

Gradients in PM2. 5 over India: Five city study. Urban Climate

Radiative forcing of black carbon over eastern India

Chemical characterization of PM10 and PM2. 5 mass concentrations emitted by heterogeneous traffic

Analysis and interpretation of particulate matter -PM10, PM2.5 and PM1 emissions from the heterogeneous traffic near an urban roadway

NOAA's HYSPLIT atmospheric transport and dispersion modeling system

An introduction to boundary layer meteorology

Diurnal and seasonal characteristics of aerosol ionic constituents over an urban location in western India: secondary aerosol formation and meteorological influence

Characteristics of black carbon concentration at a metropolitan city located near land-ocean boundary in Eastern India

Variability in atmospheric particulates and meteorological effects on their mass concentrations over Delhi

Chemical characterization of atmospheric particulate matter in Delhi, India, part II: Source apportionment studies using

Diurnal and seasonal variations of black carbon and PM2. 5 over New Delhi, India: influence of meteorology

Characteristics of absorbing aerosols during winter foggy period over the National Capital Region of Delhi: Impact of planetary boundary layer dynamics and solar radiation flux

Record heavy PM2. 5 air pollution over China

The association between PM 2.5 exposure and daily outpatient visits for allergic rhinitis: evidence from a seriously air-polluted environment

Three-year, 5 km resolution China PM2. 5 simulation: Model performance evaluation

The ion chemistry and the source of PM2. 5 aerosol in Beijing

WHO Global Ambient Air Quality Database (update

WHO Air quality guidelines for particulate matter, ozone, nitrogen dioxide and sulfur dioxide, Global update

The impact of PM2. 5 on the human respiratory system

Comparison of ground-based PM2. 5 and PM10 concentrations in China, India, and the US

On the association between outdoor PM2. 5 concentration and the seasonality of tuberculosis for Beijing and

The authors are thankful to the National Atmospheric Research Laboratory (NARL) andMinistry of Earth Sciences (MoES) for providing the necessary support. We acknowledge
