Mercury cycling in coastal metropolitan areas on the west coast of India becomes complex due to the combined effects of both intensive domestic anthropogenic emissions and marine air masses. The present study is based on yearlong data of continuous measurements of gaseous elemental mercury (GEM) concentration concurrent with meteorological parameters and some air pollutants at a coastal urban site in Mumbai, on the west coast of India, for the first time. The concentration of GEM was found in a range between 2.2 and 12.3 ng/m3, with a mean of 3.1 ± 1.1 ng/m3, which was significantly higher than the continental background values in the Northern Hemisphere ( 1.5 ng/m3). Unlike particulates, GEM starts increasing post-winter to peak during the monsoon and decrease towards winter. July had the highest concentration of GEM followed by October, and a minimum in January. GEM exhibited a distinct diurnal cycle, mainly with a broad peak in the early morning, a narrow one by nightfall, and a minimum in the afternoon. The peaks and their timing suggest the origin of urban mobility and the start of local activities. A positive correlation between SO2, PM2.5, temperature, relative humidity, and GEM indicates that emissions from local industrial plants in the Mumbai coastal area. Principal component analysis (PCA) and cluster analysis (CA) confirm this fact. Monthly back trajectory analysis showed that air mass flows are predominantly from the Arabian Sea and local human activities. Assessment of human health risks by USEPA model reveals that the hazardous quotient, HQ < 1, implies negligible carcinogenic risk. GEM observations in Mumbai during the study period are below the World Health Organization’s (WHO) safe limit (200 ng/m3) for long-term inhalation.

Routine observations of surface ozone (O3) and its precursors (NO, NO2, NOx) were taken over Bengaluru, a southern megacity, India, for 4 years period between January 2015 and December 2018. The seasonal variations of O3, NO, NO2, and NOx have been analysed to understand the short-term variability of the pollutants at this site. The magnitude of O3 varied significantly by season, with maximum concentration during winter (13.07 ppbv) and minimum concentration during monsoon (9.52 ppbv). The highest concentration was observed in the post-monsoon season (17.38 ppbv) for NO, while NO2 and NOx showed the highest (41.75, 50.42 ppbv) in the winter season. The lowest concentrations of NO (5.70 ppbv), NO2 (30.43 ppbv) and NOx(36.28 ppbv) were observed in summer. An estimate was performed to determine the site's VOC-NOx sensitivity, using the TNMHC/NOX ratio as a photochemical measure. This ratio indicates that the study region is NOX responsive in all seasons. Analysis was done on the effects of meteorological factors such as temperature, water vapour, and ventilation coefficient on pollutants. Higher correlation of O3 with temperature showed the role of photochemical reactions in the formation of ozone and water vapour content leads to the removal of ozone concentration. The influence of meteorological variables on NO2 and TNMHC did not appear to be very significant. An analysis of CAMS data with real-time measurements of ozone and oxides of nitrogen showed that ozone is significantly correlated, while nitrogen oxides are not significantly correlated with CAMS data.

The current study compares black carbon radiative effects at the densely populated plain station, Varanasi and the lesser populated plateau station Ranchi with large forest cover but with numerous open coal mines. While the measured average black carbon mass density (BC) reduces from February to March at Ranchi following an increase in convective mixing, it is observed to increase by 150% from February to March in Varanasi, as transport from northeast forest fires increases. It is observed that absorption due to black carbon of non-fossil fuel origin is prevalent throughout the day, in Varanasi, while this contribution is most significant during post sunset hours in Ranchi. Radiative forcing, estimated hourly using chemical model (to derive BC-aod) and radiative transfer model, indicates that at least 5% of the incoming radiation is always cutoff during any time of the day in Varanasi while this is about 4% in Ranchi. BC effectively causes an apparent delayed sunrise by reducing the incoming radiation on the plains of Indo Gangetic Basin (IGB) by up to 25% at the daybreak. An estimate of crop loss due to cut off in radiation, using an empirical formula for crop yield as a function of radiation, indicates a possible loss of more than a quintal per hectare considering anthesis (February) and maturity (March) periods for the winter wheat in both the IGB stations with consistently higher losses in Varanasi.

Aerosol physiochemical properties over a varied mining plateau region at the eastern end of a monsoon trough are reported for the first time and analyzed at different time scales. Aerosol optical depth (single scattering albedo, SSA) is found to be 0.49 (0.9) in pre-monsoon, 0.4 (0.94) in monsoon, 0.46 (0.92) in post-monsoon, and 0.36 (0.89) in winter, with an annual mean of 0.43 (0.91). The volume-size distribution is tri-modal, with 0.02 (ultra-fine), 0.2 (accumulation) and 7 (coarse) µm, but with seasonal signatures. The angstrom exponent (AE) varies along with the AOD, especially in winter, although they are inversely related to each other during monsoons; the increase in size may be due to the effect of humidity. AODbc varies between 13.4

The Black carbon (BC) and Brown carbon (BrC) concentration has been measured over Srinagar (Garhwal) in central Himalayas during October 2020 to September 2021 periods. The average BC mass was 2.59 +/- 1.96 mu g m- 3 and its absorption coefficients were abundant at shorter wavelength. BC seasonal variation exhibited a significant variability, with highest during winter (4.54 +/- 2.64 mu g m- 3) followed by pre-monsoon (2.69 +/- 2.04 mu g m- 3) and post-monsoon (1.93 +/- 0.91 mu g m- 3) while lowest was observed in the monsoon (1.05 +/- 0.54 mu g m- 3). Relatively high contribution of total spectral light absorption coefficient (Abs lambda) was observed (75.94 Mm-1) at 370 nm than longer wavelength (16.86 Mm-1) at 950 nm. The BrC contribution was higher at 370 nm (32.50 Mm-1) to the total babs (lambda), while at higher wavelengths it has extensively decreased (2.54 Mm-1 at 660 nm). Seasonally, the absorption coefficient of BC and BrC was greater in winter (83.99 and 68.37 Mm-1) while lowest in monsoon (19.38 and 9.27 Mm-1), respectively. The babs BrC/babs (t) ratio revealed higher contribution of BrC in winters. The secondary brown carbon (BrCsec) and primary brown carbon (BrCpri) contributed 43.16 % and 56.88 % towards the total BrC Abs (lambda) at 370 nm with higher in winter and lowest in monsoon, respectively. BrCsec and BrCprim has shown higher contribution in evening (18.00-20.00 h) and in morning (09.00-11.00 h) hours. The average radiative forcing (RF) of BC was 36.11 +/- 6.99 Wm-2, 2.19 +/- 1.22 Wm-2 and -33.92 +/- 5.96 Wm-2 at the atmosphere (ATM), Top of the Atmosphere (TOA), and at the Surface (SUR), respectively.

The organic carbon (OC) and elemental carbon (EC) were characterized in different urban regions and categorized by diverse predominant sources. This study investigates seasonal and interlocation variability of OC and EC concentration and their role in light extinction coefficient (bext) in Indian cities. OC and EC exhibit significant spatial variability (p < 0.05) with high loading of EC at Delhi (20.80 ± 5.30 µg m− 3) followed by Hyderabad (12.18 ± 4.96 µg m− 3) and Pune (10.36 ± 4.77 µg m− 3) while OC was abundant at Jabalpur (50.24 ± 4.23 µg m− 3) followed by Udaipur (49.56 ± 6.46 µg m− 3). The total carbonaceous aerosols (TCA) was the highest at Delhi (Mean = 87.42 ± 53.44 µg m− 3) along with EC (Mean = 20.80 ± 5.30 µg m− 3), followed by Hyderabad (TCA: 48.37 ± 25.50 µg m− 3 and EC: 12.18 ± 4.96 5.30 µg m− 3). The lowest TCA was found to be at Pune (TCA: 43.36 ± 25.60 µg m− 3 and EC: 5.75 ± 5.46 µg m− 3). Fractional EC percentage contribution to TC in Delhi is 10–15

System of Air quality and weather Forecasting And Research (SAFAR-Mumbai) network hourly observations of air pollutants spanning half a decade are subjected to a Generalized Additive Model (GAM) to investigate the non-linear relationship of PM2.5 to other precursor gases and meteorological parameters. The Effective Degrees of Freedom (EDF), a statistic of GAM quantifying the non-linear relationship, reveals that PM2.5 is highly non-linearly dependent on NH3, SO2, and CO as inferred from the response curves exhibiting at least one inflexion point and threshold responses. PM2.5 is linearly related to NO2 (EDF = 1), while it is non-linearly related to other gases and meteorological parameters (EDF > 2). The frequency distribution of PM2.5 shows a sharp peak at 25 mu g m(-3) in monsoon and broad peaks in pre-monsoon (peak at 50 mu g m(-3)), post-monsoon (peak at 60 mu g m(-3)) and winter (peak at 100 mu g m(-3)) indicating relatively large variability in winter and post-monsoon. Chemical analysis of PM2.5 shows a high contribution of four major ions in the order of SO42- > Cl- > Na+ > NH4+, mainly associated with anthropogenic and marine sources. The average sulphate oxidation ratio (SOR), nitrogen oxidation ratio (NOR), and neutralization ratio (NR) are up to 0.3, 0.04, and 0.27, respectively, indicating that SO2 is contributing majorly to secondary aerosol production. Monthly concertation of PM2.5, Ozone (O-3), CO, NO2, SO2, and NH3 shows U-shaped variability with maximum in winter and minimum in monsoon.

Black carbon (BC) concentrations were measured over a semi-urban location in Srinagar (Garhwal) in the central Himalaya from October 2019 to September 2020. The BC ranged between 0.7 and 3.4 µg m−3 in different months. The highest BC concentration was observed in the post-monsoon season. The long-range transport and biomass burning (BB) were found to be the major reason for BC over the region. The k values over the region ranged between − 0.004 and 0.004 in different months and indicate that aged BC was present during monsoon periods because of long-range transport. However, fresh BC was present during November, December and February (relatively fresh BC in other months). The BB contribution was up to 58%. Brown carbon (BrC) showed a similar seasonal pattern as black carbon. The BC values in the COVID-19 lockdown period were analyzed and were low compared to very high values in the same season a year before. The influence of long-range transport and forest fire incidents on BC mass concentrations is illustrated in detail. Hence, this study provides an insight into the amount of BC production due to human activity. The BC of anthropogenic origin over this region should be controlled because of its proximity to adjacent glaciers to avoid excessive BC transport to these vulnerable regions.

The variability of PM2.5 concentrations obtained from the air quality monitoring stations (AQMS) established at six different environments of the Pune Metropolitan Region (PMR), situated in the western part of India, is analyzed for the period 2014–2018. The PM2.5 concentrations showed an increasing trend at almost all locations within the city during the 5 years. Significant features observed were that the green/background location showed a declining trend in PM2.5 concentrations. However, the city's industrial area indicated an increase in PM2.5 concentrations over the years. The seasonal bivariate plot of PM2.5 showed that the winter season has the highest concentration, and also at low wind speeds a high concentration was observed, indicative of the local sources. Concentrated weighted trajectory analysis indicated that regional sources due to long-range transport also played a role in the PM2.5 mass concentration. The wavelet power spectrum of PM2.5 showed 2–4 day oscillations and 30–50 day oscillations associated with Madden-Julian oscillations.

The current study is designed to simultaneously assess for the first time similarities and differences in pollutant escalation (especially fireworks) in four mega and metro cities in India, i.e., Delhi, Ahmedabad, Mumbai and Pune, during the most important Indian festival, Diwali. The four cities are networked in the System of Air Quality and Weather Forecasting And Research (SAFAR). The data was collected through online and cumulative sampling. Particulates were analyzed for concentration trends, chemical speciation, and trace gas variations. The attitude and culture of the inhabitants in each city decided the amplitude and duration of the event. On Diwali day, PM2.5 and PM10 (maximum) in Delhi increased by 353% and 213%, respectively, compared to pre-Diwali day. The increment in PM2.5 in Pune and Ahmedabad is 50% of that in Delhi, whereas, in Mumbai, it is 1/7th of Pune. NO2 in Delhi surpassed the permissible concentration during Diwali night. Metal content (K, Mg, Na, Mn and Pb) in PM2.5 nearly doubled in all cities due to firecrackers. Prevailing meteorological conditions controlled the dispersal of pollutants. 'Ventilation Coefficient' appears to be deterministic as a pollutant sink except for wet removal. The health concern is assessed through inhalation dose (6–12 pm peak period), Delhi faced quadruple dose on Diwali day over pre-Diwali day, and it reduced close to triple on post-Diwali day. The study elucidates the need for city-specific multi-mode information to design effective control measures to curb festivity-related air pollution. • The networked cities showed specific peak times and concentrations on the event day • Online and gravimetric samplings of PM2.5 agree well • Weather and peak pollution magnitude determine dispersion efficiency at each station • Post-Diwali inhalation dose remains considerably high in Delhi and Ahmedabad

Lockdowns enforced amid the pandemic facilitated the evaluation of the impact of emission reductions on air quality and the production regime of O3 under NOx reduction. Analysis of space-time variation of various pollutants (PM10, PM2.5, NOx, CO, O3 and VOC or TNMHC) through the lockdown phases at eight typical stations (Urban/Metro, Rural/high vegetation and coastal) is carried out. It reveals how the major pollutant (PM10 or PM2.5 or O3, or CO) differs from station to station as lockdowns progress depending on geography, land-use pattern and efficacy of lockdown implementation. Among the stations analyzed, Delhi (Chandnichowk), the most polluted (PM10 = 203 μgm-3; O3 = 17.4 ppbv) in pre-lockdown, experienced maximum reduction during the first phase of lockdown in PM2.5 (-47%), NO2 (-40%), CO (-37%) while O3 remained almost the same (2% reduction) to pre-lockdown levels. The least polluted Mahabaleshwar (PM10 = 45 μgm-3; O3 = 54 ppbv) witnessed relatively less reduction in PM2.5 (-2.9%), NO2 (-4.7%), CO (-49%) while O3 increased by 36% to pre-lockdown levels. In rural stations with lots of greenery, O3 is the major pollutant attributed to biogenic VOC emissions from vegetation besides lower NO levels. In other stations, PM2.5 or PM10 is the primary pollutant. At Chennai, Jabalpur, Mahabaleshwar and Goa, the deciding factor of Air Quality Index (AQI) remained unchanged, with reduced values. Particulate matter, PM10 decided AQI for three stations (dust as control component), and PM2.5 decided the same for two but within acceptable limits for stations. Improvement of AQI through control of dust would prove beneficial for Chennai and Patiala; anthropogenic emission control would work for Chandani chowk, Goa and Patiala; emission control of CO is required for Mahabaleshwar and Thiruvanathapuram. Under low VOC/NOx ratio conditions, O3 varies with the ratio, NO/NO2, with a negative (positive) slope indicating VOC-sensitive (NOx-sensitive) regime. Peak O3 isopleths as a function of NOx and VOC depicting distinct patterns suggest that O3 variation is entirely non-linear for a given NOx or VOC.

Source apportionment study of PM2.5 using positive matrix factorization was performed to identify the emission characteristic from different sectors (sub-urban residential, industrial and rapidly urbanizing) of Delhi during winter. Chemical characterization of PM2.5 included metals (Ca, Cd, Cr, Cu, Fe, K, Mg, Mn, Na, Ni, Pb and Zn), water soluble ionic compounds (WSICs) (Cl, NO3, SO42 and NH4+) and Carbon partitions (OC, EC). Particulates (PM2.5) were collected on filter twice daily for stable and unstable atmospheric conditions, at the locations with specific characteristics, viz. Ayanagar, Noida and Okhla. Ions solely occupied 50% of the total PM2.5 concentration. Irrespective of location, high correlation between OC and EC (0.871-0.891) at p <= 0.1 is observed. Relatively lower ratio of NO3/SO4 at Ayanagar (0.696) and Okhla (0.84) denotes predominance of emission from stationary sources rather than mobile sources like that observed at Noida (1.038). Using EPA PMF5.0, optimum factors for each location are fixed based on error estimation (EE). Crustal dust, vehicular emission, biomass burning and secondary aerosol are the major contributing sources in all the three locations. Incineration contributes about 19% at Ayanagar and 18% at Okhla. Metal industries in Okhla contribute about 19% to PM2.5. These specific local emissions with considerable potency are to be targeted for long-term policymaking. Considerable secondary aerosol contribution (15%-24%) indicates that gaseous emissions also need to be reduced to improve air quality.

While local anthropogenic emission sources contribute largely to deteriorate metro air quality, long range transport can also play a significant role in influencing levels of pollutants, particularly carbon monoxide (CO) that has a relatively long life span. A nationwide lockdown of two months imposed across India amid COVID-19 led to a dramatic decline in major sources of emissions except for household, mainly from cooking. This initially led to declined levels of CO in two of the largest megacities of India, Delhi and Mumbai under stable weather conditions, followed by a distinctly different variability under the influence of prevailing mesoscale circulation. We hereby trace the sources of CO from local emissions to transport pathways and interpret the observed variability in CO using the interactive WRF-Chem model and back trajectory analysis. For this purpose, COVID-19 emission inventory of CO has been estimated. Model results indicate a significant contribution from externally generated CO in Delhi from surrounding regions and an unusual peak on 17th May amid lockdown due to long range transport from the source region of biofuel emissions in central India. However, the oceanic winds played a larger role in keeping CO levels in check in a coastal megacity Mumbai which otherwise has high CO emissions from household sources due to a larger share of urban slums. Keeping track of evolving carbon-intensive pathways can help inform government responses to the COVID-19 pandemic to prioritize controls of emissions sources.

Abstract In the present study, continuous measurements of Surface Ozone (O3), Oxides of Nitrogen (NOx (NO + NO2)), and carbon monoxide (CO), monitored at five different locations in Delhi National Capital Region have been studied for the period 2013–2019. The five monitoring locations used are namely IMD Lodi Road, IGI Airport Palam, CV Raman Dheerpur, CRRI Mathura Road, and NCMRWF Noida. The average hourly concentration of O3, NO, NO2, CO, NOx (NO + NO2), and OX(NO2 + O3) are found in the range of 32.44 ppb to 36.57 ppb, 19.46 to 28.09 ppb, 20.83 to 26.89 ppb,1.67 to 1.89 ppm,43.04 to 54.99 ppb, and 54.06 to 60.99 ppb respectively during the study period. Diurnal variation of NOx and CO Concentrations show higher values during the morning (0600-0900h) and late evening (1900-2400h) hours while the highest concentrations of ozone have been observed during afternoon hours. The relationship between NO, NO2, and surface O3 as a function of NOx has also been examined during daylight hours (0500hrs IST to 1900 hrs IST) and chemical coupling of the three species i.e. NO, NO2 and O3 have been studied. The ground-level concentration of Ozone have been found to decrease with increasing NOx concentration during daytime. The variations in concentrations of oxidants (NO2 + O3) with the concentration of [NOx] have been studied to examine the contributing pollution sources of oxidants at all the study sites. The average rate of change of O3 concentrations (dO3/dt) has been examined at all five locations. The monthly and diurnal variation of oxidants [OX] at all the study locations has shown a strong positive correlation with temperature whereas a negative correlation with humidity.

The Megacity of Delhi, home to 19 million inhabitants, is infamous for its poor air quality mainly due to anthropogenic emissions While the COVID-19 pandemic is a health emergency, lockdown due to it saw an unprecedented decline in emission sources of pollutants by ~85%-90% in Delhi, resulting in sharp decline in the concentration of majority of pollutants Here we report the experimental estimate of baseline level that is defined as the minimum level reached after lockdown under consistent fair weather condi-tion of major criteria pollutants This may be consi-dered as an indicator of the background levels to which the population is chronically exposed The con-sequences of such chronic air pollution exposure are excess respiratory and cardiovascular morbidity and mortality which are reported to be more serious than severe pollution episodes by epidemiologists As the lockdown which was imposed on 24 March 2020, was extended during April and May, we present the pre-vailing ambient pollution levels and compare them with the baseline levels Results are based on India’s largest monitoring network of 34 stations in Delhi The findings are critical for policymakers to fine-tune ambient air quality standards and regulations leading to the development of effective risk management poli-cies and control strategies © 2020 All rights reserved

Aerosol-cloud interactions and feedbacks play an important role in modulating cloud development, microphysical and optical properties thus enhancing or reducing precipitation over polluted/pristine regions. The lockdown enforced on account of Covid-19 pandemic is a unique opportunity to verify the influence of drastic reduction in aerosols on cloud development and its vertical distribution embedded in identical synoptic conditions. Cloud bases measured by ceilometer in Delhi, the capital of India, are observed to propagate from low level to higher levels as the lockdown progresses. It is explained in terms of trends in temporal variation of cloud condensation nuclei (CCN) and precursor gases to secondary hygroscopic aerosols. The large reduction (47%) in CCN estimated from aerosol extinction coefficient during the lockdown results in upward shift of cloud bases. Low clouds with bases located below 3 km are found to have reduced significantly from 63% (of total clouds distributed in the vertical) during pre-lockdown to 12% in lockdown period (less polluted). Cloud base height is found to have an inverse correlation with CCN (r= -0.64) and NO2/NH3 concentrations (r= -0.7). The role of meteorology and CCN in modulating the cloud vertical profiles is discussed in terms of anomalies of various controlling factors like lifting condensation level (LCL), precipitable water content (PWC) and mixing layer height (MLH). (C) 2020 Elsevier B.V. All rights reserved.

A drastic decline in the sources of emissions of pollutants under COVID-19 induced lockdown resulted in an unprecedented trends in most hazardous pollutants PM2.5, PM10 and NO2 in India. To realize the impact of lockdown in the concentrations of PM2.5, PM10 and NO2, we compared the trend of lockdown period (20nd March to 15th April) with several (3–7) years of past data in four Indian mega cities (Delhi, Pune, Mumbai, and Ahmedabad) of different micro-climate and geography. The significant reduction in the concentrations of NO2 in the ranges of ~60–65% is noticed in four megacities within the lockdown period when compared with the averaged data of past years. However, relatively low reduction in PM2.5 (~25–50%) and PM10 (~36–50%) is observed and city to city variation is found to be significant. The prevailing secondary aerosol formation and enhancement of any natural source of emissions could be some factors preventing PM2.5 levels to go down significantly. Under near negligible fossil fuel emission, contrary to the expectation, an increase in the ratio as compared to normal scenario is observed in Delhi on some days whereas on some selected days, PM2.5/PM10 ratio is found to decline significantly.

Unfortunately, Fig. 5 has been published incorrect. Please find the corrected figure below.

The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is rapidly spreading across the globe due to its contagion nature. We hereby report the baseline permanent levels of two most toxic air pollutants in top ranked mega cities of India. This could be made possible for the first time due to the unprecedented COVID-19 lockdown emission scenario. The study also unfolds the association of COVID-19 with different environmental and weather markers. Although there are numerous confounding factors for the pandemic, we find a strong association of COVID-19 mortality with baseline PM2.5 levels (80% correlation) to which the population is chronically exposed and may be considered as one of the critical factors. The COVID-19 morbidity is found to be moderately anti-correlated with maximum temperature during the pandemic period (-56%). Findings although preliminary but provide a first line of information for epidemiologists and may be useful for the development of effective health risk management policies.

Global picture of modification of land surface fluxes due to aerosol loading, especially over different landscapes, is not yet clear. The impact of aerosols on surface fluxes over Indo-Gangetic Basin, a highly polluted region, is studied by selecting four stations in the north Indian plains that are agriculturally heterogeneous in nature, viz., short crop area, industrial area, seasonal agricultural area, and permanent dense canopy area. Data from satellite (Moderate Resolution Imaging Spectroradiometer (MODIS)), model (Global Land Data Assimilation (GLDAS-NOAH), National Centre for Environmental Prediction/Oregon State University/Airforce/Hydrology Research Laboratory, and reanalysis (MERRA-Modern-Era Retrospective analysis for Research and Applications) as well as ground observations (AERONET (AErosol RObotic NETwork) and Dibrugarh University measurements) on daily and monthly scales are used to validate and assess the aerosol-induced modification of surface fluxes. To ensure that satellite products are reasonably accurate, aerosol optical depth (AOD) and evapotranspiration (ET) from MODIS are validated against ground truth of AOD and ET from GLDAS-NOAH and found to have correlations of 0.90 and 0.76, respectively. Aerosol radiative forcing (ARF) was calculated as the difference between 'clear sky' and 'clean clear sky' of net radiation (R) at surface obtained from MERRA. Since surface fluxes are perturbed directly by ARF on a daily scale with maximum effect during 'sunlit' hours, change in fluxes is calculated on the daily scale. The impact of aerosols alone on latent heat flux (LE) is quantified by using the linear relation between R and LE along with the aerosol-induced reduction of net radiation. Latent heat flux and R from GLDAS-NOAH showed an excellent correlation between 0.75 and 0.97. The impact of aerosols on surface fluxes, represented as perturbed latent and sensible heat fluxes normalized by ARF, is found to be followed an asymptotic curve against Bowen ratio (B) which indicates aridity of the land surface. This study shows that the aerosols reduce LE by 60% of ARF over the permanent dense canopy with considerable soil moisture (B = 0.5), while the same amount of ARF reduces LE by 25% only over the semi-arid region (B = 2) over the north Indian plains for the period from October 2008 to May 2009.