Role of meteorology in seasonality of air pollution in megacity Delhi, India

Source attribution of carbon monoxide over Northern India during crop residue burning period over Punjab

National Capital Territory of Delhi and its satellite cities suffer from poor air quality during the post-monsoon months of October-November. In this study, a novel attempt is made to estimate the contribution of different emission sources (industrial, residential, power generation, transportation, biomass burning, photochemical production, lateral transport, etc.) towards the criteria air pollutant carbon monoxide (CO) concentration over North India. Multiple simulations of the WRF-Chem model with a tagged tracer approach with different inputs (6 anthropogenic emission inventories and 3 biomass burning emission inventories) were used. The model performance was evaluated against the MOPITT retrieved CO surface concentration. Analysis of model simulated CO over North India suggests that anthropogenic emissions contribute around 32-49% to surface CO concentration while crop residue burning contributes 27-44% of which 80% originates from Punjab. For Delhi, the contribution from anthropogenic sources is dominant (53-77%) of which 10-28% is from the domestic sector and 14-55% is from the transport sector. Agricultural waste burning contributes about 15-30% to Delhi's surface CO concentration (of which 75% originates from Punjab). Crop residue burning emission is a chief source of CO over Punjab with a contribution of about 56-76%. The results suggest that industrial, transport, and domestic sector activities are more responsible for increased CO levels over New Delhi and surrounding regions than crop residue burning over Punjab. Furthermore, critical meteorological parameters like 10 m wind speed, boundary layer height, 2 m temperature, total precipitation, and relative humidity were evaluated against CO concentration to understand their impact on CO distribution. Results conclude that deteriorating air quality over the North Indian region is caused by a combination of prevailing meteorological factors (such as slow winds, shallow mixing layer, and cold temperatures) and man-made emissions.

Estimating black carbon levels using machine learning models in high-concentration regions

Black carbon (BC) is emitted into the atmosphere during combustion processes, often in conjunction with emissions such as nitrogen oxides (NOx) and ozone (O3), which are also by-products of combustion. In highly polluted regions, combustion processes are one of the main sources of aerosols and particulate matter (PM) concentrations, which affect the radiative budget. Despite the high relevance of this air pollution metric, BC monitoring is quite expensive in terms of instrumentation and of maintenance and servicing. With the aim to provide tools to estimate BC while minimising instrumentation costs, we use machine learning approaches to estimate BC from air pollution and meteorological parameters (NOx, O3, PM2.5, relative humidity (RH), and solar radiation (SR)) from currently available networks. We assess the effectiveness of various machine learning models, such as random forest (RF), support vector regression (SVR), and multilayer perceptron (MLP) artificial neural network, for predicting black carbon (BC) mass concentrations in areas with high BC levels such as Northern Indian cities (Delhi and Agra), across different seasons. The results demonstrate comparable effectiveness among the models, with the multilayer perceptron (MLP) showing the most promising results. In addition, the comparability between estimated and monitored BC concentrations was high. In Delhi, the MLP shows high correlations between measured and modelled concentrations during winter (R2: 0.85) and post-monsoon (R2: 0.83) seasons, and notable metrics in the pre-monsoon (R2: 0.72). The results from Agra are consistent with those from Delhi, highlighting the consistency of the neural network's performance. These results highlight the usefulness of machine learning, particularly MLP, as a valuable tool for predicting BC concentrations. This approach provides critical new opportunities for urban air quality management and mitigation strategies and may be especially valuable for megacities in medium- and low-income regions.

Temporal dynamics of urban air pollutants and their correlation with associated meteorological parameters: an investigation in northern Indian cities

This study delves into the intricate dynamics of air pollution in the rapidly expanding northern regions of India, examining the intertwined influences of agricultural burning, industrialization, and meteorological conditions. Through comprehensive analysis of key pollutants (PM2.5, PM10, NO2, SO2, CO, O3) across ten monitoring stations in Uttar Pradesh, Haryana, Delhi, and Punjab, a consistent pattern of high pollution levels emerges, particularly notable in Delhi. Varanasi leads in SO2 and O3 concentrations, while Moradabad stands out for CO levels, and Jalandhar for SO2 concentrations. The study further elucidates the regional distribution of pollutants, with Punjab receiving significant contributions from SW, SE, and NE directions, while Haryana and Delhi predominantly face air masses from SE and NE directions. Uttar Pradesh's pollution sources are primarily local, with additional inputs from various directions. Moreover, significant negative correlations (p < 0.05) between PM10, NO2, SO2, O3, and relative humidity (RH) underscore the pivotal role of meteorological factors in shaping pollutant levels. Strong positive correlations between PM2.5 and NO2 (0.71 to 0.93) suggest shared emission sources or similar atmospheric conditions in several cities. This comprehensive understanding highlights the urgent need for targeted mitigation strategies to address the multifaceted drivers of air pollution, ensuring the protection of public health and environmental sustainability across the region.

Association between sea-land breeze and particulate matter in five coastal urban locations in India

Particulate matter less than 2.5 μm particle diameter (PM2.5) is the most significant environmental issue globally. PM2.5 is an integral component of air quality monitoring and management, human health, weather, climate, and epidemiological research. In this work, we investigate the seasonal variation in PM2.5 mass concentrations and the association between the sea-land breeze system and particulate matter in five coastal urban locations in India (Kolkata, Visakhapatnam, Chennai, Thiruvananthapuram, and Mumbai). The relative occurrence of high PM2.5 mass concentrations was the greatest during the winter season (December through February) while the relative occurrence of low PM2.5 mass concentrations was the greatest during the monsoon season (June through September). Amongst locations, Kolkata experiences the highest PM2.5 loading in winter while Thiruvananthapuram experiences the lowest PM2.5 loading in monsoon. Indo-Gangetic Plain (IGP) outflow onto the Bay of Bengal significantly impacts locations along the eastern coast of India with reduced impact from north (Kolkata) to south (Chennai). The sea-breeze component analysis revealed daily cycles of the sea-land breeze with varying magnitudes of the breeze between the different seasons. Overall, we found a negative association between the sea-land breeze magnitude and PM2.5 mass concentrations, implying that the weakened sea-land breeze may deteriorate air quality in coastal locations due to poor ventilation. The vertical profiles of aerosol extinction showed elevated aerosol layers within 1 km from the surface in almost all locations. The decreasing trend in the land-sea temperature contrast in coastal locations is expected to deteriorate air quality in coastal locations in the warming future. Nevertheless, critical analyses using ground-based remote sensing techniques are required for a better understanding the impact of sea-land breeze dynamics on air quality in coastal locations.

Determining hotspots of gaseous criteria air pollutants in Delhi airshed and its association with stubble burning

Transboundary pollutant transport is considered as one of the primary factors causing the seasonal air quality deterioration in Delhi, India's capital. The highest standard deviations exceeding days in winter for NO2 (7.14-9.63%) and SO2 (4.04-7.42%) in 2019-2022 underscore the role of meteorological conditions in Delhi's pollution. In contrast, the post-monsoon season shows the highest pollutant exceedance days (4.52-8.00%) for CO due to stubble burning (SB) in Punjab (68,902 fires/year). Despite the government's assertions of decreasing SB events (14.68%), the city's CO exceedance days persistently rose by 6.36%. CAMS data is used for assessing contribution hotspots through back-trajectory analysis at multiple heights. An overlap hotspot of 111 sq. km area is identified in the Southeast parts of Punjab that have a higher contribution to the CO levels in Delhi during the post-monsoon season of 2019. Similarly, hotspots are also observed for SO2 over industrial areas of Punjab during the post-monsoon and pre-monsoon seasons. The same seasons show similar contributing patterns for NO2 highlighting the influence of consistent emission patterns and meteorological conditions. The clear delineation of hotspots using the receptor model at multiple heights coupled with source apportionment studies will assist decision-makers in addressing the pollution sources outside Delhi.

A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023

Land use regression (LUR) models are widely used in epidemiological and environmental studies to estimate humans' exposure to air pollution within urban areas. However, the early models, developed using linear regressions and data from fixed monitoring stations and passive sampling, were primarily designed to model traditional and criteria air pollutants and had limitations in capturing high-resolution spatiotemporal variations of air pollution. Over the past decade, there has been a notable development of multi-source observations from low-cost monitors, mobile monitoring, and satellites, in conjunction with the integration of advanced statistical methods and spatially and temporally dynamic predictors, which have facilitated significant expansion and advancement of LUR approaches. This paper reviews and synthesizes the recent advances in LUR approaches from the perspectives of the changes in air quality data acquisition, novel predictor variables, advances in model-developing approaches, improvements in validation methods, model transferability, and modeling software as reported in 155 LUR studies published between 2011 and 2023. We demonstrate that these developments have enabled LUR models to be developed for larger study areas and encompass a wider range of criteria and unregulated air pollutants. LUR models in the conventional spatial structure have been complemented by more complex spatiotemporal structures. Compared with linear models, advanced statistical methods yield better predictions when handling data with complex relationships and interactions. Finally, this study explores new developments, identifies potential pathways for further breakthroughs in LUR methodologies, and proposes future research directions. In this context, LUR approaches have the potential to make a significant contribution to future efforts to model the patterns of long- and short-term exposure of urban populations to air pollution.

Assessment of meteorological parameters on air pollution variability over Delhi

In this study, the relationships between meteorological parameters (relative humidity, wind speed, temperature, planetary boundary layer, and rainfall) and air pollutants (particulate matter and gaseous pollutants) have been evaluated during a 3-year period from 2019 to 2021. Diffusion and dispersion of air contaminants were significantly influenced by meteorology over the capital city. The results of correlation matrix and principal component analysis (PCA) suggest a season's specific influence of meteorological parameters on atmospheric pollutants' concentration. Temperature has the strongest negative impact on pollutants' concentration, and all the other studied meteorological parameters negatively (reduced) as well as positively (increased) impacted the air pollutants' concentration. A two-way process was involved during the interaction of pollutants with relative humidity and wind speed. Due to enhanced moisture-holding capacity during non-monsoon summers, particles get larger and settle down on the ground via dry deposition processes. Winter's decreased moisture-holding capacity causes water vapour coupled with air contaminants to remain suspended and further deteriorate the quality of the air. High wind speed helps in the dispersion and dilution but a high wind speed associated with dust particles may increase the pollutants' level downwind side. The PM2.5/PM10 variation revealed that the accumulation effect of relative humidity on PM2.5 was more intense than PM10. Daily average location-specific rainfall data revealed that moderate to high rainfall has a potential wet scavenging impact on both particulate matters and gaseous pollutants.

AQI prediction using layer recurrent neural network model: a new approach

The air quality index (AQI) prediction is important to evaluate the effects of air pollutants on human health. The airborne pollutants have been a major threat in Delhi both in the past and coming years. The air quality index is a figure, based on the cumulative effect of major air pollutant concentrations, used by Government agencies, for air quality assessment. Thus, the main aim of the present study is to predict the daily AQI one year in advance through three different neural network models (FF-NN, CF-NN and LR-NN) for the year 2020 and compare them. The models were trained using AQI values of previous year (2019). In addition to main air pollutants like PM10/PM2.5, O3, SO2, NOx, CO and NH3, the non-criteria pollutants and meteorological data were also included as input parameter in this study. The model performances were assessed using statistical analysis. The key air pollutants contributing to high level of daily AQI were found to be PM2.5/PM10, CO and NO2. The root mean square error (RMSE) values of 31.86 and 28.03 were obtained for the FF-NN and CF-NN models respectively whereas the LR-NN model has the minimum RMSE value of 26.79. LR-NN algorithm predicted the AQI values very closely to the actual values in almost all the seasons of the year. The LR-NN performance was also found to be the best in post-monsoon season i.e., October and November (maximum R2 = 0.94) with respect to other seasons. The study would aid air pollution control authorities to predict AQI more precisely and adopt suitable pollution control measures. Further research studies are recommended to compare the performance of LR-NN model with statistical, numerical and computational models for accurate air quality assessment.

Effects of stubble burning and firecrackers on the air quality of Delhi

Every year at the onset of winter season (October-November), crop residue/parali/stubble burning starts in Punjab and Haryana, leading to heavy air pollution in Delhi, and adversely affecting human and environmental health. During this time, the combination of unfavourable meteorological conditions, additional emissions from stubble burning, and firework activities in this area causes the air quality to further deteriorate. In this study, we have attempted to understand the influence of parali and firecracker incidents on air pollutants' variability over Delhi during the last three years (2020 to 2022). For this purpose, daily average particulate matter and gaseous pollutants data were fetched from the Central Pollution Control Board (CPCB), and daily total fire counts and fire radiative power (FRP) data were retrieved from NASA's Fire Information for Resource Management System (FIRMS). A bigger area of severe burning is suggested by higher FRP values and higher fire counts in the middle of November in all the years considered. Three years satellite-based FIRMS data over Punjab and Haryana show the highest number of active fire counts in 2021 (n = 80,505) followed by 2020 (n = 75,428), and 2022 (n = 49,194). More than 90% parali burning incidents were observed in Punjab state only despite the considerable variability in numbers among the years. The significant effect of parali burning was seen on pollutant concentration variability. As the number of fire count increases or decreases in Punjab and Haryana, there is a corresponding increase or decrease in the particulate matter concentration with a time lag of few days (1 to 2 days). The trend in backward air mass trajectories suggests that the variable response time of pollutants' concentration is due to local and distant sources with different air mass speeds. Our estimates suggest that stubble burning contributes 50-75% increment in PM2.5 and 40 to 45% increase in PM10 concentration between October and November. A good positive correlation between PM2.5, PM10, NOX, and CO and fire counts (up to 0.8) suggests a strong influence of stubble burning on air quality over Delhi. Furthermore, the firecracker activities significantly increase the concentration of particulate matter with ~100% increment in PM2.5 and ~55% increment in PM10 mass concentrations for a relatively shorter period (1 to 2 days).

Very high particulate pollution over northwest India captured by a high-density in situ sensor network

Exposure to particulate matter less than 2.5 µm in diameter (PM2.5) is a cause of concern in cities and major emission regions of northern India. An intensive field campaign involving the states of Punjab, Haryana and Delhi national capital region (NCR) was conducted in 2022 using 29 Compact and Useful PM2.5 Instrument with Gas sensors (CUPI-Gs). Continuous observations show that the PM2.5 in the region increased gradually from < 60 µg m-3 in 6-10 October to up to 500 µg m-3 on 5-9 November, which subsequently decreased to about 100 µg m-3 in 20-30 November. Two distinct plumes of PM2.5 over 500 µg m-3 are tracked from crop residue burning in Punjab to Delhi NCR on 2-3 November and 10-11 November with delays of 1 and 3 days, respectively. Experimental campaign demonstrates the advantages of source region observations to link agricultural waste burning and air pollution at local to regional scales.

Analysis of aerosol liquid water content and its role in visibility reduction in Delhi

Aerosols undergo significant changes due to water uptake under high RH conditions, leading to changes in physical, optical, and chemical properties. Detailed assessment and investigation are needed to understand better aerosol liquid water content (ALWC) characteristics in highly polluted regions like Delhi. Therefore, in this study, we examined the mass concentration and the factors governing the ALWC associated with PM_2.5 in Delhi for two winters (Dec 2019 to Jan 2020 and Dec 2020 to Feb 2021) using the real-time measurements of NR-PM_2.5 from Aerodyne aerosol chemical speciation monitor (ACSM) and the application of thermodynamic modeling (ISORROPIA II). The average NR-PM_2.5 mass concentration in the 2020-2021 winter was 152 μg/m³, about 50 % higher than the average mass concentration of 102 μg/m³ in 2019-2020. Consequently, the ALWC was also 60 % higher during 2020-2021, with an average mass concentration of 150 μg/m³. ALWC increased exponentially with RH and is significant when RH > 80 %. Further, all the inorganic components of NR-PM_2.5 were found to contribute significantly to ALWC uptake; however, the relative contribution varied in different RH conditions. Ammonium sulphate dominated the ALWC uptake among the inorganic components at low RH, but ammonium nitrate was the dominant contributor at high RH. The decreased chloride mass fraction in inorganics in the recent winters reduced its relative contribution to ALWC. High ALWC mass concentration during high PM_2.5 and high RH leads to a significant reduction in visibility. We further validated this visibility reduction by estimating the enhanced light scattering coefficient (f(RH)) and found that the hygroscopic growth is responsible for the enhanced visibility reduction during high RH conditions (> 85 %) when light scattering efficiency increased by a factor of >3.5. Sensitivity tests of f(RH) on mass concentration of inorganic salts showed that all the salts contributed almost equally. As revealed in our study, variations in PM_2.5 mass concentration and composition despite similar meteorological conditions between different winters indicate changing regional aerosol emissions. Therefore, long-term observations of ALWC and PM_2.5 chemical composition are required to arrive at actionable measures and mitigation strategies. Further, the focus should be on reducing the overall inorganic mass concentrations of PM_2.5 in general, decreasing the absolute ALWC, and improving visibility.

Effect of COVID-19-induced lockdown on NO2 pollution using TROPOMI and ground-based CPCB observations in Delhi NCR, India

The present study investigates the reduction in nitrogen dioxide (NO2) levels using satellite-based (Sentinel-5P TROPOMI) and ground-based (Central Pollution Control Board) observations of 2020. The lockdown duration, monthly, seasonal and annual changes in NO2 were assessed comparing the similar time period in 2019. The study also examines the role of atmospheric parameters like wind speed, air temperature, relative humidity, solar radiation and atmospheric pressure in altering the monthly and annual values of the pollutant. It was ascertained that there was a mean reduction of ~ 61% (~ 66.5%), ~ 58% (~ 51%) in daily mean NO2 pollution during lockdown phase 1 when compared with similar period of 2019 and pre-lockdown phase in 2020 from ground-based (satellite-based) measurements. April month with ~ 57% (~ 57%), summer season with ~ 48% (~ 32%) decline and an annual reduction of ~ 20% (~ 18%) in tropospheric NO2 values were observed (p < 0.001) compared to similar time periods of 2019. It was assessed that the meteorological parameters remained almost similar during various parts of the year in 2019 and 2020, indicating a negligent role in reducing the values of atmospheric pollution, particularly NO2 in the study area. It was concluded that the halt in anthropogenic activities and associated factors was mainly responsible for the reduced values in the Delhi conglomerate. Similar work can be proposed for other pollutants to holistically describe the pollution scenario as an aftermath of COVID-19-induced lockdown.

Air pollution in Delhi, India: It’s status and association with respiratory diseases

The policymakers need research studies indicating the role of different pollutants with morbidity for polluted cities to install a strategic air quality management system. This study critically assessed the air pollution of Delhi for 2016–18 to found out the role of air pollutants in respiratory morbidity under the ICD-10, J00-J99. The critical assessment of Delhi air pollution was done using various approaches. The mean PM_2.5 and PM₁₀ concentrations during the measurement period exceeded both national and international standards by a wide margin. Time series charts indicated the interdependence of PM_2.5 and PM₁₀ and connection with hospital visits due to respiratory diseases. Violin plots showed that daily respiratory disease hospital visits increased during the winter and autumn seasons. The winter season was the worst from the city’s air pollution point of view, as revealed by frequency analyses. The single and multi-pollutant GAM models indicated that short-term exposure to PM₁₀ and SO₂ led to increased hospital visits due to respiratory diseases. Per 10 units increase in concentrations of PM₁₀ brought the highest increase in hospital visits of 0.21% (RR: 1.00, 95% CI: 1.001, 1.002) at lag0-6 days. This study found the robust effect of SO₂ persisted in Delhi from lag0 to lag4 days and lag01 to lag06 days for single and cumulative lag day effects, respectively. While every 10 μg m^-3 increase of SO₂ concentrations on the same day (lag0) led to 32.59% (RR: 1.33, 95% CI: 1.09, 1.61) rise of hospital visits, the cumulative concentration of lag0-1 led to 37.21% (RR: 1.37, 95% CI:1.11, 1.70) rise in hospital visits which further increased to even 83.33% (RR: 1.83, 95% CI:1.35, 2.49) rise at a lag0-6 cumulative concentration in Delhi. The role of SO₂ in inducing respiratory diseases is worrying as India is now the largest anthropogenic SO₂ emitter in the world.

A novel seasonal index-based machine learning approach for air pollution forecasting

Novel machine learning models (MLMs) using the seasonal indexing approach that captures the variation in air quality caused due to meteorological changes have been used to provide short-term, real-time forecasts of PM_2.5 concentration for one of the most polluted air quality control regions (AQCR) in the capital city of Delhi. Two MLMs-multi-linear regression and random forest-have been developed for using time series data for 1-h and 24-h average PM_2.5 concentration. Short-term, real-time forecasts have been made using the developed models. Various model performance evaluation indices indicate satisfactory model performance. R² values for the hourly and daily models varied between 0.95 and 0.72 and between 0.76 and 0.68 for the 1st to 5th h/day, respectively. The lagged values of PM_2.5 concentration (persistence) and the hourly and daily indices are the most influential variables for the forecasts for immediate time steps. In contrast, seasonal indices become more important with the forecasting time horizon. The developed models can be used for making short-term, real-time air quality forecasts and issuing a warning when the pollution levels go beyond acceptable limits.

Spatialization and Prediction of Seasonal NO2 Pollution Due to Climate Change in the Korean Capital Area through Land Use Regression Modeling

Urbanization is causing an increase in air pollution leading to serious health issues. However, even though the necessity of its regulation is acknowledged, there are relatively few monitoring sites in the capital metropolitan city of the Republic of Korea. Furthermore, a significant relationship between air pollution and climate variables is expected, thus the prediction of air pollution under climate change should be carefully attended. This study aims to predict and spatialize present and future NO₂ distribution by using existing monitoring sites to overcome deficiency in monitoring. Prediction was conducted through seasonal Land use regression modeling using variables correlated with NO₂ concentration. Variables were selected through two correlation analyses and future pollution was predicted under HadGEM-AO RCP scenarios 4.5 and 8.5. Our results showed a relatively high NO₂ concentration in winter in both present and future predictions, resulting from elevated use of fossil fuels in boilers, and also showed increments of NO₂ pollution due to climate change. The results of this study could strengthen existing air pollution management strategies and mitigation measures for planning concerning future climate change, supporting proper management and control of air pollution.

Gridded distribution of total suspended particulate matter (TSP) and their chemical characterization over Delhi during winter

In the present study, total suspended particulate matter (TSP) samples were collected at 47 different sites (47 grids of 5 × 5 km² area) of Delhi during winter (January-February 2019) in campaign mode. To understand the spatial variation of sources, TSP samples were analyzed for chemical compositions including carbonaceous species [organic carbon (OC), elemental carbon (EC), and water-soluble organic carbon (WSOC)], water-soluble total nitrogen (WSTN), water-soluble inorganic nitrogen (WSIN), polycyclic aromatic hydrocarbons (16 PAHs), water-soluble inorganic species (WSIS) (F^-, Cl^-, SO₄^2-, NO₂^-, NO₃^-, PO₄^3-, NH₄⁺, Ca²⁺, Mg²⁺, Na⁺, and K⁺), and major and minor trace elements (B, Na, Mg, Al, P, S, Cl, K, Ca, Ti, Fe, Zn, Cr, Mn, Cu, As, Pd, F, and Ag). During the campaign, the maximum concentration of several components of TSP (996 μg/m³) was recorded at the Rana Pratap Bagh area, representing a pollution hotspot of Delhi. The maximum concentrations of PAHs were recorded at Udhyog Nagar, a region close to heavily loaded diesel vehicles, small rubber factories, and waste burning areas. Higher content of Cl^- and Cl^-/Na⁺ ratio (>1.7) suggests the presence of nonmarine anthropogenic sources of Cl^- over Delhi. Minimum concentrations of OC, EC, WSOC, PAHs, and WSIS in TSP were observed at Kalkaji, representing the least polluted area in Delhi. Enrichment factor <5.0 at several locations and a significant correlation of Al with Mg, Fe, Ti, and Ca and C/N ratio indicated the abundance of mineral/crustal dust in TSP over Delhi. Principal component analysis (PCA) was also performed for the source apportionment of TSP, and extracted soil dust was found to be the major contributor to TSP, followed by biomass burning, open waste burning, secondary aerosol, and vehicular emissions.

Spatio-temporal variation and sensitivity analysis of aerosol particulate matter during the COVID-19 phase-wise lockdowns in Indian cities

At the pandemic of COVID-19, the movement of business and other non-essential activities were majorly restricted at the end of March 2020 in India and continued in different lockdown phases until June 2020. By categorically, studying sensitivity towards anthropogenic factors with other environmental implications in urban Indian cities during phase-wise lockdown scenarios will pave the way for a refined Clean Air Programme (CAP). In this study, the aerosol particulate matter variations between the lockdown phases in both spatial and temporal scales have been explored along with cities exceeding national ambient air quality (NAAQ) standards covering different geographical regions of India for their air quality level. The results of the spatial pattern of Copernicus Atmosphere Monitoring System (CAMS) near-real-time data showed a negative change both in Aerosol Optical Depth (AOD) (-0.2 to 0.1) and black carbon AOD (bcAOD) (-0.9 to -0.75). The changes were evident in successive phases of lockdown with an overall AOD reduction of about 70-90%. Southern urban cities showed a significant impact of mobile sources from temporal analysis than other cities. Principal Component Analysis (PCA) for effects of pollutants by anthropogenic factors (mobile and point source) and meteorological factors (wind speed, wind direction, solar radiation, relative humidity) revealed the two significant driving factors. PM reduction was about 50-70%, predominantly due to anthropogenic factors. The factor analysis revealed the influence of meteorological factors between the major urban cities (Delhi, Kolkata, Mumbai, Chennai, Bengaluru, and Hyderabad). Cities that exceed NAAQ standard performed well during phase-wise lockdowns, exceptional to cities in Gangetic plain. This study helps to frame region-specific strategic action plans for the CAP.

Diurnal variability in the spectral characteristics and sources of water-soluble brown carbon aerosols over Delhi

It is well established that light-absorbing organic aerosols (commonly known as brown carbon, BrC) impact climate. However, uncertainties remain as their contributions to absorption at different wavelengths are often ignored in climate models. Further, BrC exhibits differences in absorption at different wavelengths due to the variable composition including varying sources and meteorological conditions. However, diurnal variability in the spectral characteristics of water-soluble BrC (hereafter BrC) is not yet reported. This study presents unique measurement hitherto lacking in the literature. Online measurements of BrC were performed using an assembled system including a particle-into-liquid sampler, portable UV-Visible spectrophotometer with liquid waveguid capillary cell, and total carbon analyzer (PILS-LWCC-TOC). This system measured the absorption of ambient aerosol extracts at the wavelengths ranging from 300 to 600 nm with 2 min integration time and water-soluble organic carbon (WSOC) with 4 min integration time over a polluted megacity, New Delhi. Black carbon, carbon monoxide (CO), nitrogen oxides (NOx), and the chemical composition of non-refractory submicron aerosols were also measured in parallel. Diurnal variability in absorption coefficient (0.05 to 65 Mm^-1), mass absorption efficiency (0.01 to 3.4 m^-2 gC^-1) at 365 nm, and absorption angstrom exponent (AAE) of BrC for different wavelength range (AAE_300-400: 4.2-5.8; AAE_400-600: 5.5-8.0; and AAE_300-600: 5.3-7.3) is discussed. BrC chromophores absorbing at any wavelength showed minimum absorption during afternoon hours, suggesting the effects of boundary layer expansion and their photo-sensitive/volatile nature. On certain days, a considerable presence of BrC absorbing at 490 nm was observed during nighttime that disappears during the daytime. It appeared to be associated with secondary BrC. Observations also infer that BrC species emitted from the biomass and coal burning are more absorbing among all sources. A fraction of BrC is likely associated with trash burning, as inferred from the spectral characteristics of Factor-3 from the PMF analysis of BrC spectra. Such studies are essential in understanding the BrC characteristics and their further utilization in climate models.

Performance evaluation of air pollution control device at traffic intersections in Delhi

Urban air pollution and exposure-related health impacts are being noticed and discussed very intensely in India. On the other hand, source-specific control is the primary focus for policymakers; however, diverse and complex sources make it difficult to immediately see the action and consequent impacts on better air quality. Many cities across the world have witnessed high air pollution levels at traffic junctions, more so in all Indian cities. Site-specific air pollution reduction can be a promising solution for managing the pollution level at highly polluted locations. CSIR-National Environmental Engineering Research Institute, India, has designed and developed Wind Augmentation and purifYing Unit (WAYU) to remove particulate and gaseous pollutants from urban hot spots such as traffic locations. In the present study, the authors attempted to evaluate the performance of two different designs of WAYU for the removal of particulate matters from polluted air at different traffic locations in Delhi City, the national capital territory of India. The performance analyses show that the current design of WAYU removes PM₁₀ and PM_2.5 concentrations in the range of 34-49% and 19-25%, respectively from the inlet air. The total PM collected from all WAYU devices was 34.19 kg from 120,557 operating hours' at all the sampling sites. The PM removal rate depends on the size-segregated particulate matter pollution load in the ambient air.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13762-021-03641-3.

Housing, health and energy: a characterisation of risks and priorities across Delhi's diverse settlements

Improved housing has the potential to advance health and contribute to the Sustainable Development Goals. Research examining housing, health and energy use in low-income countries is limited; understanding these connections is vital to inform interventions for healthy sustainable human settlements. This paper investigates the low-income setting of Delhi, where rapid urbanisation, a varied climate, high pollution levels, and a wide variation in housing quality could result in significant energy use and health risks. Drawing on approaches from health and the built environment and existing data and literature, a characterisation of energy use and health risks for Delhi's housing stock is completed. Four broad settlement types were used to classify Delhi housing and energy use calculations and health risk assessment were performed for each variant. Energy use is estimated to be nearly two times higher per household among planned housing compared with other settlement types. Health risks, however, are found to be largest within informal slum settlements, with important contributions from heat and particulate matter across all settlements. This paper highlights intervention priorities and outlines the need for extensive further research, particularly through data gathering, to establish evidence to accelerate achieving healthy, sustainable and equitable housing in Delhi.

Impact of COVID-19 induced lockdown and unlock down phases on the ambient air quality of Delhi, capital city of India

The present study deals with the impact of the pandemic outbreak of COVID-19 on the ambient air quality in the capital city of India. Real-time data were collected from eight continuous ambient air quality monitoring stations measuring important air quality parameters (NO₂, PM₁₀ and PM_2.5). Results revealed that the city's air quality had improved significantly during the lockdown period due to COVID-19 outbreak. The concentration of gaseous and particulate matter during the lockdown period (March-May 2020) declined significantly compared with the preceding years' data from the same timeframe. However, the ambient air quality deteriorates with the onset of unlocking phases and post-monsoon season (October 2020). Higher concentration of NO₂, PM₁₀ and PM_2.5 were recorded at industrial (S1 and S2) and hotspot (S4 and S5) sites. The lowest concentrations of studied pollutants were observed during the first phase of lockdown (March 24 - May 14, 2020). The present study, once again, establishes the direct effect of anthropogenic activities and deteriorating ambient air quality of Delhi.

Regression-based flexible models for photochemical air pollutants in the national capital territory of megacity Delhi

Modelling photochemical pollutants, such as ground level ozone (O₃), nitric oxide (NO) and nitrogen dioxide (NO₂), in urban terrain was proven to be cardinal, chronophagous and complex. We built linear regression and random forest regression models using 4-years (2015-2018; hourly-averaged) observations for forecasting O₃, NO and NO₂ levels for two scenarios (1-month prediction (for January 2019) and 1-year prediction (for 2019)) - with and without the impact of meteorology. These flexible models have been developed for, both, localised (site-specific models) and combined (indicative of city-level) cases. Both models were aided with machine learning, to reduce their time-intensity compared to models built over high-performance computing. O₃ prediction performance of linear regression model at the city level, under both cases of meteorological consideration, was found to be significantly poor. However, the site-specific model with meteorology performed satisfactorily (r = 0.87; RK Puram site). Further, during testing, linear regression models (site-specific and combined) for NO and NO₂ with meteorology, show a slight improvement in their prediction accuracies when compared to the corresponding equivalent linear models without meteorology. Random forest regression with meteorology performed satisfactorily for indicative city-level NO (r = 0.90), NO₂ (r = 0.89) and O₃ (r = 0.85). In both regression techniques, increased uncertainty in modelling O₃ is attributed to it being a secondary pollutant, non-linear dependency on NO_x, VOCs, CO, radicals, and micro-climatic meteorological parameters. Analysis of importance among various precursors and meteorology have also been computed. The study holistically concludes that site-specific models with meteorology perform satisfactorily for both linear regression and random forest regression.

Air Quality Before and After COVID-19 Lockdown Phases Around New Delhi, India

BACKGROUND

The COVID-19 pandemic has had a deep global impact, not only from a social and economic perspective, but also with regard to human health and the environment. To restrict transmission of the virus, the Indian government enforced a complete nationwide lockdown except for essential services and supplies in phases from 25 March to 31 May 2020. Ambient air quality in and around New Delhi, one of the most polluted cities of world, was also impacted during this period.

OBJECTIVE

The aim of the present study was to assess and understand the impact of four different lockdown phases (LD1, LD2, LD3 and LD4) on five air pollutants (particulate matter (PM) PM2.5, PM10, nitrogen oxide (NOx), sulfur dioxide (SO2) and ozone (O3)) compared to before lockdown (BLD) at 13 air monitoring stations in and around New Delhi.

METHODS

Secondary data on five criteria pollutants for 13 monitoring stations in and around New Delhi for the period 1 March to 31 May 2020 was accessed from the Central Pollution Control Bard, New Delhi. Data were statistically analyzed across lockdown phases, meteorological variables, and prevailing air sources around the monitoring stations.

RESULTS

Pollutant concentrations decreased during LD1 compared to BLD except for O3 at all stations. PM2.5 and PM10 remained either close to or higher than the National Ambient Air Quality Standards (NAAQS) due to prevailing high-speed winds. During lockdown phases, NO2 decreased, whereas O3 consistently increased at all stations. This was a paradoxical situation as O3 is formed via photochemical reactions among NOx and volatile organic compounds. Principal component analysis (PCA) extracted two principal components (PC1 and PC2) which explained up to 80% of cumulative variance in data. PM2.5, PM10 and NO2 were associated with PC1, whereas PC2 had loadings of either O3 only or O3 and SO2 depending upon monitoring station.

CONCLUSIONS

The present study found that air pollutants decreased during lockdown phases, but these decreases were specific to the site(s) and pollutant(s). The decrease in pollutant concentrations during lockdown could not be attributed completely to lockdown conditions as the planetary boundary layer increased two-fold during lockdown compared to the BLD phase. Such restrictions could be applied in the future to control air pollution but should be approached with caution.

COMPETING INTERESTS

The authors declare no competing financial interests.

Spatio-temporal variability of near-surface air pollutants at four distinct geographical locations in Andhra Pradesh State of India

India is highly vulnerable to air pollution in the recent decade, especially urban areas with rapidly growing urbanisation and industrialisation. Here, we present spatio-temporal variability of air pollutants at four distinct locations in Andhra Pradesh State of India. The mean concentrations of air pollutants were generally higher at Visakhapatnam site than Amaravati, Rajahmundry, and Tirumala sites. The mean concentration of particulate matter of diameter less than 2.5 μm (PM_2.5) was higher at Visakhapatnam site (48.5 ± 27.3 μg/m³) by a factor of about 1.6 as compared to Tirumala site (29.5 ± 17 μg/m³). On the contrary, the mean concentrations of oxides of nitrogen (NO_x, 70.3 ± 28.1 μg/m³) and ammonia (NH₃, 20.5 ± 9.2 μg/m³) were higher at Tirumala by a factor of about 1.4 and 1.9, respectively, as compared to Visakhapatnam (49 ± 5 μg/m³ and 10.7 ± 5 μg/m³). This was mainly attributed to higher vehicular emissions at Tirumala site. PM_2.5, carbon monoxide (CO), NOx, and sulfur dioxide (SO₂) showed distinct seasonal variation, with higher concentrations in winter followed by post-monsoon, pre-monsoon and monsoon. The Concentration Weighted Trajectory analysis of PM_2.5 based on 5-days backward air mass trajectories showed that all sites experienced northeast air mass flow indicative of the outflow from Indo-Gangetic Plain, particularly in the post-monsoon and winter seasons. The Continuous Wavelet Transform analysis further showed that higher variations in PM_2.5 concentrations occurring at a regular interval from a week to 16 days at both Tirumala and Visakhapatnam sites, while weekly periods are dominant over Amaravati and Rajahmundry sites with 95% significance during post-monsoon and winter seasons. Overall, our results underline heterogeneity in air pollution emission sources and influx of pollutants from distant sources, which would be useful when formulating the policies and mitigation procedures for this region.

Real-time measurement and source apportionment of elements in Delhi's atmosphere

Delhi, the capital of India, suffers from heavy local emissions as well as regional transport of air pollutants, resulting in severe aerosol loadings. To determine the sources of these pollutants, we have quantified the mass concentrations of 26 elements in airborne particles, measured by an online X-ray fluorescence spectrometer with time resolution between 30 min and 1 h. Measurements of PM₁₀ and PM_2.5 (particulate matter <10 μm and < 2.5 μm) were conducted during two consecutive winters (2018 and 2019) in Delhi. On average, 26 elements from Al to Pb made up ~25% and ~19% of the total PM₁₀ mass (271 μg m^-3 and 300 μg m^-3) in 2018 and 2019, respectively. Nine different aerosol sources were identified during both winters using positive matrix factorization (PMF), including dust, non-exhaust, an S-rich factor, two solid fuel combustion (SFC) factors and four industrial/combustion factors related to plume events (Cr-Ni-Mn, Cu-Cd-Pb, Pb-Sn-Se and Cl-Br-Se). All factors were resolved in both size ranges (but varying relative concentrations), comprising the following contributions to the elemental PM₁₀ mass (in % average for 2018 and 2019): Cl-Br-Se (41.5%, 36.9%), dust (27.6%, 28.7%), non-exhaust (16.2%, 13.7%), S-rich (6.9%, 9.2%), SFC1 + SFC2 (4%, 7%), Pb-Sn-Se (2.3%, 1.66%), Cu-Cd-Pb (0.67%, 2.2%) and Cr-Ni-Mn (0.57%, 0.47%). Most of these sources had the highest relative contributions during late night (22:00 local time (LT)) and early morning hours (between 03:00 to 08:00 LT), which is consistent with enhanced emissions into a shallow boundary layer. Modelling of airmass source geography revealed that the Pb-Sn-Se, Cl-Br-Se and SFC2 factors prevailed for northwest winds (Pakistan, Punjab, Haryana and Delhi), while the Cu-Cd-Pb and S-rich factors originated from east (Nepal and Uttar Pradesh) and the Cr-Ni-Mn factor from northeast (Uttar Pradesh). In contrast, SFC1, dust and non-exhaust were not associated with any specific wind direction.

Environmental Association of Burning Agricultural Biomass in the Indus River Basin

Intensification of smog episodes, following harvesting of paddy crops in agricultural plains of the Indus basin in the Indian subcontinent, are often attributed to farming practice of burning standing stubble during late autumn (October, November) months. Biomass burning (paddy stubble residual) is a preferred technique to clear farmlands for centuries by farmers in that basin. However, despite stable agricultural landholding and yield, smog is being increasingly associated with burning agricultural biomass, thus creating a paradox. Here, we show that the concentration of smog (NOx, PM2.5, SO2) in the ambient air exceeds the safe threshold limits throughout the entire year in the region. This study argues that agricultural biomass burning is an ephemeral event in the basin that may act as a catalyst to a deteriorated air quality in the entire region. Results further demonstrate that simultaneous saturation of air pollutants along with high ambient moisture content and low wind speeds following the monsoon season are strongly related to aggravated smog events. Findings from this study should help make holistic mitigation and intervention policies to monitor air quality for sustainability of public health in agricultural regions where farming activities are a dominant economic driver for society.

Volatile organic compound measurements point to fog-induced biomass burning feedback to air quality in the megacity of Delhi

We report the first ambient measurements of thirteen VOCs for investigations of emissions and air quality during fog and non-fog wintertime conditions at a tower site (28.57° N, 77.11° E, 220 m amsl) in the megacity of Delhi. Measurements of acetonitrile (biomass burning (BB) tracer), isoprene (biogenic emission tracer in daytime), toluene (a traffic exhaust tracer) and benzene (emitted from BB and traffic), together with soluble and reactive oxygenated VOCs such as methanol, acetone and acetaldehyde were performed during the winters of 2015-16 and 2016-17, using proton transfer reaction mass spectrometry. Remarkably, ambient VOC composition changes during fog were not governed by solubility. Acetaldehyde, toluene, sum of C8-aromatics (e.g. xylenes), sum of C9-aromatics (e.g. trimethyl benzenes) decreased by ≥30% (>95% confidence interval), whereas acetonitrile and benzene showed significant increases by 20% (>70% confidence interval), even after accounting for boundary layer dilution. During fog, the lower temperatures appeared to induce an emissions feedback from enhanced open BB within Delhi for warming, releasing both gaseous and aerosol pollutants with consequences for fog chemistry, sustenance and intensity. The potential feedback is important to consider for improving current emission parametrizations in models used for predicting air quality and fog in such atmospheric environments.

Characterization of PM2.5 in Delhi: role and impact of secondary aerosol, burning of biomass, and municipal solid waste and crustal matter

Delhi is one among the highly air polluted cities in the world. Absence of causal relationship between emitting sources of PM_2.5 and their impact has resulted in inadequate actions. This research combines a set of innovative and state-of-the-art analytical techniques to establish relative predominance of PM_2.5 sources. Air quality sampling at six sites in summer and winter for 40 days (at each site) showed alarmingly high PM_2.5 concentrations (340 ± 135 μg/m³). The collected PM_2.5 was subjected to chemical speciation including ions, metals, organic and elemental carbons which followed application of chemical mass balance technique for source apportionment. The source apportionment results showed that secondary aerosols, biomass burning (BMB), vehicles, fugitive dust, coal and fly ash, and municipal solid waste burning were the important sources. It was observed that secondary aerosol and crustal matter accounted for over 50% of mass. The PM_2.5 levels were not solely result of emissions from Delhi; it is a larger regional problem caused by contiguous urban agglomerations. It was argued that emission reduction of precursors of secondary aerosol, SO₂, NO_x, and volatile organic compounds, which are unabated, is essential. A substantial reduction in BMB and suspension of crustal dust is equally important to ensure compliance with air quality standards.

Heavy truck restrictions and air quality implications in São Paulo, Brazil

This study quantified the effects of traffic restrictions on diesel fuel heavy vehicles (HVs) on the air quality of the Bandeirantes corridor using hourly data obtained by continuous monitoring of traffic and air quality at sites located on this avenue. The study addressed the air quality of a city impacted by vehicular emissions and that PM₁₀ and NO_X concentrations are mainly due to diesel burning. Data collection was split into two time periods, a period of no traffic constraint on HVs (Nov 2008 and 2009) and a period of constraint (Nov 2010, 2011 and 2012). We found that pollutants on this corridor, mainly PM₁₀ and NO_X, decreased significantly during the period from 2008 to 2012 (28 and 43%, 15.8 and 86.9 ppb) as a direct consequence of HV traffic restrictions (a 72% reduction). Rebound effects in the form of increased traffic of light vehicles (LVs) during this time had impacts on the concentration levels, explaining the differences between rates of reduction in HV traffic and pollutants. Reductions in the number of trucks resulted in longer travel times and increased traffic congestion as a consequence of the modal shift towards LVs. We found that a 51% decrease in PM₁₀ (28.8 μg m^-3) was due to a reduction in HV traffic (vehicle emissions were estimated to be 71% of total sources, 40.1 μg m^-3). This percentage was partially offset by 10% more PM₁₀ emissions related to an increase in LV traffic, while other causes, such as climatic conditions, contributed to a 13% increase in PM₁₀ concentrations. The relationships analyzed in this research served to highlight the need to apply urban transport policies aimed at decreasing pollutant concentrations in São Paulo, especially in heavily congested urban corridors on working days.

The influence of odd-even car trial on fine and coarse particles in Delhi

The odd-even car trial scheme, which reduced car traffic between 08.00 and 20.00 h daily, was applied from 1 to 15 January 2016 (winter scheme, WS) and 15-30 April 2016 (summer scheme, SS). The daily average PM_2.5 and PM₁₀ exceeded national standards, with highest concentrations (313 μg m^-3 and 639 μg m^-3, respectively) during winter and lowest (53 μg m^-3 and 130 μg m^-3) during the monsoon (June-August). PM concentrations during the trials can be interpreted either as reduced or increased, depending on the periods used for comparison purposes. For example, hourly average net PM_2.5 and PM₁₀ (after subtracting the baseline concentrations) reduced by up to 74% during the majority (after 1100 h) of trial hours compared with the corresponding hours during the previous year. Conversely, daily average PM_2.5 and PM₁₀ were higher by up to 3-times during the trial periods when compared with the pre-trial days. A careful analysis of the data shows that the trials generated cleaner air for certain hours of the day but the persistence of overnight emissions from heavy goods vehicles into the morning odd-even hours (0800-1100 h) made them probably ineffective at this time. Any further trial will need to be planned very carefully if an effect due to traffic alone is to be differentiated from the larger effect caused by changes in meteorology and especially wind direction.

PM2.5 Population Exposure in New Delhi Using a Probabilistic Simulation Framework

This paper presents a Geographical Information System (GIS) based probabilistic simulation framework to estimate PM2.5 population exposure in New Delhi, India. The framework integrates PM2.5 output from spatiotemporal LUR models and trip distribution data using a Gravity model based on zonal data for population, employment and enrollment in educational institutions. Time-activity patterns were derived from a survey of randomly sampled individuals (n = 1012) and in-vehicle exposure was estimated using microenvironmental monitoring data based on field measurements. We simulated population exposure for three different scenarios to capture stay-at-home populations (Scenario 1), working population exposed to near-road concentrations during commutes (Scenario 2), and the working population exposed to on-road concentrations during commutes (Scenario 3). Simulated annual average levels of PM2.5 exposure across the entire city were very high, and particularly severe in the winter months: ∼200 μg m(-3) in November, roughly four times higher compared to the lower levels in the monsoon season. Mean annual exposures ranged from 109 μg m(-3) (IQR: 97-120 μg m(-3)) for Scenario 1, to 121 μg m(-3) (IQR: 110-131 μg m(-3)), and 125 μg m(-3) (IQR: 114-136 μ gm(-3)) for Scenarios 2 and 3 respectively. Ignoring the effects of mobility causes the average annual PM2.5 population exposure to be underestimated by only 11%.

An investigation of potential regional and local source regions affecting fine particulate matter concentrations in Delhi, India

In this study, potential regional and local sources influencing PM2.5 (particulate matter with an aerodynamic diameter >2.5 μm) concentrations in Delhi, India, are identified and their possible impact evaluated through diverse approaches based on study of variability of synoptic and local airflow patterns that transport aerosol concentrations from these emission sources to an urban receptor site in Delhi, India. Trajectory clustering of 72-hr and 48-hr back trajectories simulated at arrival heights of 500 m and 100 m, respectively, every hour for representative years 2008-2010 are used to assess the relative influence of long-distance, regional, and subregional sources on this site. Nonparametric statistical procedures are employed on trajectory clusters to better delineate various distinct regional pollutant source regions. Trajectory clustering and concentration-weighted trajectory (CWT) analyses indicate that regional and subregional PM2.5 emission sources in neighboring country of Pakistan and adjacent states of Punjab, Haryana, and Uttar Pradesh contribute significantly to the total surplus of aerosol concentrations in the Delhi region. Conditional probability function and Bayesian approach used to identify local source regions have established substantial influence from highly urbanized satellite towns located southwest (above 25%) and southeast (above 45%) of receptor location. There is significant seasonal variability in synoptic and local air circulation patterns, which is discerned in variability in seasonal concentrations. Mean of daily averaged PM2.5 concentrations at the Income Tax Office (ITO) receptor site over Delhi at 95% confidence level is highest in winter, ranging between 209 and 185 μg m⁻³ for the entire study period. The annual variability in air transport pathways is more in winter than in other seasons. Year-to-year variability is present in aerosol concentrations, especially during winter, with standard deviations varying from a minimum of 60 µg m⁻³ in winter 2009 to a maximum of 109 µg m⁻³ in winter 2010.

Seasonal characteristics of SO2, NO2, and CO emissions in and around the Indo-Gangetic Plain

Anthropogenic emissions of sulfur dioxide (SO2), nitrogen dioxide (NO2), and carbon monoxide (CO) exert significant influence on local and regional atmospheric chemistry. Temporal and spatial variability of these gases are investigated using surface measurements by the Central Pollution Control Board (India) during 2005-2009 over six urban locations in and around the Indo-Gangetic Plain (IGP) and supported using the satellite measurements of these gases. The stations chosen are Jodhpur (west of IGP), Delhi (central IGP), Kolkata and Durgapur (eastern IGP), Guwahati (east of IGP), and Nagpur (south of IGP). Among the stations studied, SO2 concentrations are found to be the highest over Kolkata megacity. Elevated levels of NO2 occur over the IGP stations of Durgapur, Kolkata, and Delhi. Columnar NO2 values are also found to be elevated over these regions during winter due to high surface concentrations while columnar SO2 values show a monsoon maximum. Elevated columnar CO over Guwahati during pre-monsoon are attributed to biomass burning. Statistically significant correlations between columnar NO2 and surface NO2 obtained for Delhi, Kolkata, and Durgapur along with very low SO2 to NO2 ratios (≤0.2) indicate fossil fuel combustion from mobile sources as major contributors to the ambient air over these regions.

Spatiotemporal land use regression models of fine, ultrafine, and black carbon particulate matter in New Delhi, India

Air pollution in New Delhi, India, is a significant environmental and health concern. To assess determinants of variability in air pollutant concentrations, we develop land use regression (LUR) models for fine particulate matter (PM2.5), black carbon (BC), and ultrafine particle number concentrations (UFPN). We used 136 h (39 sites), 112 h (26 sites), 147 h (39 sites) of PM2.5, BC, and UFPN data respectively, to develop separate morning (0800-1200) and afternoon (1200-1800) models. Continuous measurements of PM2.5 and BC were also made at a single fixed rooftop site located in a high-income residential neighborhood. No continuous measurements of UFPN were available. In addition to spatial variables, measurements from the fixed continuous monitoring site were used as independent variables in the PM2.5 and BC models. The median concentrations (and interquartile range) of PM2.5, BC, and UFPN at LUR sites were 133 (96-232) μg m(-3), 11 (6-21) μg m(-3), and 40 (27-72) × 10(3) cm(-3) respectively. In addition (a) for PM2.5 and BC, the temporal variability was higher than the spatial variability; (b) the magnitude and spatial variability in pollutant concentrations was higher during morning than during afternoon hours. Further, model R(2) values were higher for morning (for PM2.5, BC, and UFPN, respectively: 0.85, 0.86, and 0.28) than for afternoon models (0.73, 0.69, and 0.23); (c) the PM2.5 and BC concentrations measured at LUR sites all over the city were strongly correlated with measured concentrations at a fixed rooftop site; (d) spatial patterns were similar for PM2.5 and BC but different for UFPN; (e) population density and road variables were statistically significant predictors of pollutant concentrations; and (f) available geographic predictors explained a much lower proportion of variability in measured PM2.5, BC, and UFPN than observed in other LUR studies, indicating the importance of temporal variability and suggesting the existence of uncharacterized sources.