Search

The present study assesses trends in rainfall in Madhya Pradesh, Central India, over the long (1871–2016) as well as short (1987–2016) temporal scales. The region is home to disadvantageous groups of the population including vulnerable tribals who depend on rainfall-based agriculture and forest products. Therefore, any disruption in rainfall trends due to climate change needs to be documented for better planning. This study focuses on two meteorological subdivisions, East and West Madhya Pradesh, which receive a major portion of rainfall through the Bay of Bengal branch and the Arabian Sea branch of the Indian Summer Monsoon. The study is based on the rainfall series provided by the Indian Institute of Tropical Meteorology, Pune, India. This study used the Mann–Kendall test to detect a trend and Sen's Slope to estimate the magnitude of change in rainfall. Statistically significant negative trends were detected in total annual (− 1.125 mm y − 1) and monsoon (− 1.242 mm y − 1) rainfall over long-term observation in East Madhya Pradesh only. However, during the last 30 years, no trends have been recorded in East Madhya Pradesh. In West Madhya Pradesh, rainfall has not recorded any trend for long- as well as short-term observation periods. The non-availability of a trend in rainfall during May and October at both meteorological subdivisions shows that there is no shift in the duration of the monsoon season over long as well as short temporal observations. The decreasing rainfall trends in Eastern Madhya Pradesh over the long-term observation seem to be caused by changing patterns of tropical cyclone frequency in the Bay of Bengal. [ABSTRACT FROM AUTHOR]

Farmers in India have benefited considerably from the combination of satellite-enabled services and data obtained on the ground. The India Meteorological Department, Ministry of Earth Sciences, provides weather forecasting, agro-advisory services, agromet services, soil moisture monitoring, and agricultural extension initiatives to encourage agricultural operations in India. Indian Space Research Organization's (ISRO) also partners with the Ministry of Agriculture and Farmers Welfare on several satellite data and geographic information systems-based agricultural applications. ISRO, in collaboration with the Ministry of Agriculture and Farmers Welfare, has developed applications including horticultural crop inventory and site suitability for expansion in unutilized places; crop assessment using medium- and high-resolution satellite data; field information gathering with field photos using a smartphone application; and crop cutting experiments based on satellite-derived crop vigor information. ISRO has provided technology for FASAL and the National Agricultural Drought Assessment and Surveillance System to the Department of Agriculture Cooperation and Farmer Welfare. ISRO has also incorporated the Central Water Commission's monitoring of irrigation systems. Overall, satellite-enabled services have transformed agricultural operations in India by providing farmers with precise and timely data that enable them to make educated decisions about their crops, resulting in increased crop yields and financial returns. [ABSTRACT FROM AUTHOR]

Extreme smog in India widely impacts air quality in late autumn and winter months. While the links between emissions, air quality and health impacts are well‐recognized, the association of smog and its intensification with climatic trends in the lower troposphere, where aerosol pollution and its radiative effects manifest, are not understood well. Here we use long‐term satellite data to show a significant increase in aerosol exceedances over northern India, resulting in sustained atmospheric warming and surface cooling trends over the last two decades. We find several lines of evidence suggesting these aerosol radiative effects have induced a multidecadal (1980–2019) strengthening of lower tropospheric stability and increase in relative humidity, leading to over fivefold increase in poor visibility days. Given this crucial aerosol‐radiation‐meteorological feedback driving the smog intensification, results from this study would help inform mitigation strategies supporting stronger region‐wide measures, which are critical for solving the smog challenge in India. Plain Language Summary: Severe air pollution in India and its impacts on air quality and public health are worsening. Extreme smog episodes are frequently observed in northern India associated with the highest aerosol concentrations and hazardous visibility conditions. It is well‐known that anthropogenic emissions directly affect pollution, but it remains unclear from an observational perspective how the stability of the lower troposphere, where aerosol pollution builds up, impacts the long‐term evolution of smog. Using a multidecadal analysis of satellite, ground and reanalysis data sets, here we show sustained intensification of extreme smog associated with the strengthening of lower tropospheric stability, potentially amplified by aerosol‐induced atmospheric warming. Solving the smog crisis in India is increasingly critical given the strongly linked aerosol‐radiation‐meteorological interactions. Key Point: Past 40‐year observations reveal aerosol‐induced radiation‐meteorological feedbacks have intensified extreme smog in India [ABSTRACT FROM AUTHOR]

The increasing population and its associated amenities demand innovative devices, infrastructure, methods, plans and policies. Regional climate has a great role in deciding the air quality and energy demand, and therefore, weather and climate have an indisputable role in its consumption and storage. Here, we present the changes in trace gases and associated regional weather in India during lockdown and unlock periods of COVID-19. We observe a reduction of about 30% in sulphur dioxide (SO2) and 10–20% in aerosols in the Indo-Gangetic Plain (IGP), large cities, industrial sites, mining areas and thermal power plants during lockdown as compared to the same period in the previous year and with respect to its climatology. However, a considerable increase in aerosols is found, particularly over IGP during Unlock 1.0 (1–30 June 2020), because of the relaxation of lockdown restrictions. The analyses also show a decrease in temperature by 1–3 °C during lockdown compared to its climatology for the same period, mainly in IGP and Central India, possibly due to the significant reduction in absorbing aerosols such as black carbon and decrease in humidity during the period. The west coast, northwest and central India show reduced wind speed when compared to its previous year and climatological values, suggesting that there was a change in regional weather due to the lockdown. Energy demand in India decreased by about 25–30% during the first phase of lockdown and about 20% during the complete lockdown period. This study thus suggests that the reduction of pollution could also modify local weather, and these results would be useful for drafting policy decisions on air pollution reduction, urban development, the energy sector, agriculture and water resources. [ABSTRACT FROM AUTHOR]

Black carbon (BC) aerosols critically impact the climate and hydrological cycle. The impact of anthropogenic emissions and coastal meteorology on BC dynamics, however, remains unclear over tropical India, a globally identified hotspot. In this regard, we have performed in situ measurements of BC over a megacity (Chennai, 12° 59′ 26.5″ N, 80° 13′ 51.8″ E) on the eastern coast of India during January–June 2020, comprising the period of COVID-19-induced strict lockdown. Our measurements revealed an unprecedented reduction in BC concentration by an order of magnitude as reported by other studies for various other pollutants. This was despite having stronger precipitation during pre-lockdown and lesser precipitation washout during the lockdown. Our analyses, taking mesoscale dynamics into account, unravels stronger BC depletion in the continental air than marine air. Additionally, the BC source regime also shifted from a fossil-fuel dominance to a biomass burning dominance as a result of lockdown, indicating relative reduction in fossil fuel combustion. Considering the rarity of such a low concentration of BC in a tropical megacity environment, our observations and findings under near-natural or background levels of BC may be invaluable to validate model simulations dealing with BC dynamics and its climatic impacts in the Anthropocene. [ABSTRACT FROM AUTHOR]

In this study, three approaches namely parallel, sequential, and multiple linear regression are applied to analyze the local air quality improvements during the COVID‐19 lockdowns. In the present work, the authors have analyzed the monitoring data of the following primary air pollutants: particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO). During the lockdown period, the first phase has most noticeable impact on airquality evidenced by the parallel approach, and it has reflected a significant reduction in concentration levels of PM10 (27%), PM2.5 (19%), NO2 (74%), SO2 (36%), and CO (47%), respectively. In the sequential approach, a reduction in pollution levels is also observed for different pollutants, however, these results are biased due to rainfall in that period. In the multiple linear regression approach, the concentrations of primary air pollutants are selected, and set as target variables to predict their expected values during the city's lockdown period.The obtained results suggest that if a 21‐days lockdown is implemented, then a reduction of 42 µg m−3 in PM10, 23 µg m−3 in PM2.5, 14 µg m−3 in NO2, 2 µg m−3 in SO2, and 0.7 mg m−3 in CO can be achieved. [ABSTRACT FROM AUTHOR]

The study attempts to quantitatively understand the impact of dynamic vegetation on land-surface atmosphere interactions over spring wheat croplands in India. A new modeling tool capable of simulating these interactions was developed by incorporating the crop growth module of the Simple and Universal Crop growth Simulator (SUCROS) crop model into the Weather Research and Forecasting (WRF) mesoscale model. An earlier study had calibrated and evaluated the stand-alone SUCROS crop model with observed data for spring wheat collected from an experimental site in northwestern India. The crop growth module of the calibrated SUCROS model was implemented in the Noah-MP land module of WRF to build the coupled WRF_NOAHMP_SUCROS model. Numerical experiments were conducted with WRF_SUCROS that simulates the simultaneous evolution of meteorological drivers and crop Leaf Area Index (LAI) and the two-way interactions between these processes. These experiments were compared with WRF simulations driven by observed climatological mean LAI. These experiments only simulate the effects of changes in LAI on meteorology but not the other round. Results show that the coupled WRF_NOAHMP_SUCROS model is able to simulate the LAI better than the default dynamic vegetation module in WRF. It also produces realistic simulations of the near-surface meteorological parameters. The latent heat flux (LHF) varies directly with LAI, and sensible heat flux (SHF) varies inversely with LAI. As the crop grows, the energy transfer occurs more in latent heat flux than sensible heat flux due to increased evapotranspiration. Hence the growing crops result in near-surface cooling due to decreased Bowed Ratio. The mixing ratio is also increased due to increased latent heat flux. The uncoupled WRF model also shows similar patterns except in the juvenile crop stage where it overestimates the sensible heating and temperature but underestimates latent heat fluxes and mixing ratio. [ABSTRACT FROM AUTHOR]

A monthly rainfall dataset for India at country, regional and meteorological sub-divisional scales was developed by the Indian Institute of Tropical Meteorology (IITM) based on a fixed network of 306 rain gauges. This dataset has been constructed when long period data was not available at many locations and there was not much computing power available. This data has been used worldwide for rainfall analysis over India. In this study, this rainfall dataset has been compared with a larger network of rain gauges maintained by the India Meteorological Department (IMD) for the southwest monsoon period of 1901–2010 at meteorological sub-divisional scale. Two different rain gauge networks can give rise to divergent estimates of rainfall, in general from differences in network density or location of individual rain gauges in each network, assuming measurement errors have small effect. Although mean monthly and seasonal monsoon rainfall and their interannual variability in both IITM and IMD datasets are similar, IITM dataset shows larger difference from IMD data for several meteorological sub-divisions. The long-term trends and frequency of occurrence of deficient and excess monsoon rainfall also show considerable differences between these two rainfall datasets. Data from a sparse network is not representative at meteorological sub-divisions associated with rather larger spatial variations in the southwest monsoon rainfall. For instance, IITM dataset has 11 rain gauges compared to 147 IMD rain gauges over a meteorological sub-division—South Interior Karnataka, and mean absolute difference in monthly monsoon rainfall estimates becomes about 25% when compared for rather shorter period using station data. It is also demonstrated that inclusion of additional rain gauges substantially improves the quality of IITM monthly rainfall estimates over this specific meteorological sub-division. [ABSTRACT FROM AUTHOR]

Industrial emissions are a serious environmental problem worldwide due to particulates and toxic gases. This study aims to generate an activity-specific emission inventory and estimate emissions dispersion extent in the vicinity of the coastal industrial village by simulating the existing coke oven and pellet plant emissions using the steady-state plume model. Continuous air quality monitoring results were compared with the predicted consequential emissions for the year 2018-19. The maximum ground-level concentrations of particulate and gases within the modeling simulation domain were observed at 9005 m away from the center. They were predicted to be 116.39 µg.m-3, 79.14 µg.m-3, 52.97 µg.m-3, and 211.86 µg.m-3. Data analysis showed that air mass transport from the project to the receptor sites resulted in ambient air concentrations higher than those observed in the other sites. Overall predicted results obtained from AERMOD Cloud simulations were shown to have less bias than the measured results. They recommended considering it as appropriate for the prediction of annual average concentration. [ABSTRACT FROM AUTHOR]

High concentrations of tropospheric ozone (O3) is a serious concern in India. The generation and atmospheric dynamics of this trace gas depend on the availability of its precursors and meteorological variables. Like other parts of the world, the COVID-19 imposed lockdown and restrictions on major anthropogenic activities executed a positive impact on the ambient air quality with reduced primary pollutants/precursors load. In spite of this, several reports pointed towards a higher O3 in major Indian cities during the lockdown. The present study designed with 30 pan-Indian mega-, class I-, and class II-cities revealed critical and contrasting aspects of the geographical location, source, precursor, and meteorological variable dependency of the spatial and temporal O3 formation. This unexpected O3 increase in the major cities might forecast the probable future risks for the National Air Quality policies, especially O3 pollution management, in the Indian sub-continent. The results also pointed towards the severity of the north Indian air quality, followed by the western and eastern parts. We believe these results will definitely pave the way for researchers and policy-makers for predicting/framing regional and/or national O3 management strategies in the future. [ABSTRACT FROM AUTHOR]

The real‐time Benzene, Toluene, Ethylbenzene, and Xylenes (BTEX) concentrations were measured in a metropolitan city of India during January to May of 2020 and 2014‐2015‐2018 to assess the impact of emission reduction during the COVID‐19 lockdown. The total BTEX (∑BTEX) concentrations were 11.5 ± 9.0, 15.7 ± 16, 5.3 ± 5.0, 2.9 ± 2.0, and 0.93 ± 1.2 ppbv in January–May 2020, respectively. The evening rush hour peaks of BTEX during lockdown decreased by 4–5 times from the same period of years 2014‐2015‐2018. A significant decline in background concentrations suggests a regional‐scale reduction in anthropogenic emissions. The contributions of ∑TEX compounds to ∑BTEX increased from 42% to 59% in winter to 64%–75% during the lockdown under hot summer conditions. While emission reductions dominated during the lockdown period, the meteorological and photochemical factors may also have contributed. Meteorological influence on actual observed BTEX data was removed by normalizing with ventilation coefficient (VC). The actual ambient air reductions of 85%–90% and VC‐normalized reductions of 54%–88% of the BTEX concentrations during lockdown were estimated compared to those during the same period of 2014‐2015‐2018. The estimated changes using nighttime data, which take into account BTEX photooxidation removal, are ∼8% lower than the VC‐normalized estimates using all data. These significant reductions in BTEX concentrations are consistent with the change in people's movement as inferred from mobility data during the lockdown. Although enforced, the significant decline in ambient BTEX levels during lockdown was a good change for the air quality. The study suggests a need for more effective science‐based policies that consider local and regional factors. Plain Language Summary: Outbreaks of the COVID‐19 pandemic necessitated the implementation of strict lockdown in India, which drastically decreased anthropogenic emissions. The elevated levels of a group of aromatic volatile organic compounds known as benzene, toluene, ethylbenzene, and xylenes (BTEX) can adversely impact human health. The real‐time continuous measurements of ambient air BTEX concentrations were conducted in a major city of India during January–May 2020. The concentrations of all BTEX compounds declined drastically during the COVID‐19 lockdown period. We incorporated the VC in the ambient air BTEX concentrations to reduce the meteorological influences. During the lockdown, different BTEX compounds were reduced by 54%–88% compared to the same period during the normal years. In addition to reduced anthropogenic activities, the balance between photochemical processes and evaporative emissions seems to control BTEX concentration and composition during the lockdown period. Although enforced, the decrease of ambient BTEX concentrations was a good change for air quality as these compounds are primary pollutants and precursors for secondary pollutants. This is the most comprehensive study, investigating the impact of the lockdown on ambient BTEX concentrations in India. Key Points: Significant reductions in ambient benzene, toluene, ethylbenzene, and xylenes (BTEX) concentrations during COVID‐19 lockdown in a major city of IndiaMeteorology‐corrected BTEX concentrations during lockdown‐2020 decreased by 54%–88% from the same period of 2014–2015–2018The estimates accounting for the photo oxidation removal cause lesser reductions by ∼8% than ventilation coefficient‐normalized estimates [ABSTRACT FROM AUTHOR]

The Indo‐Gangetic Plain (IGP) is one of the dominant sources of air pollution worldwide. During winter, the variations in planetary boundary layer (PBL) height, driven by a strong radiative thermal inversion, affect the regional air pollution dispersion. To date, measurements of aerosol‐water vapor interactions, especially cloud condensation nuclei (CCN) activity, are limited in the Indian subcontinent, causing large uncertainties in radiative forcing estimates of aerosol‐cloud interactions. We present the results of a one‐month field campaign (February‐March 2018) in the megacity, Delhi, a significant polluter in the IGP. We measured the composition of fine particulate matter (PM1) and size‐resolved CCN properties over a wide range of water vapor supersaturations. The analysis includes PBL modeling, backward trajectories, receptor models and fire spots to elucidate the influence of PBL and air mass origins on aerosols. The aerosol properties depended strongly on PBL height and a simple power‐law fit could parameterize the observed correlations of PM1 mass, aerosol particle number and CCN number with PBL height, indicating PBL induced changes in aerosol accumulation. The low inorganic mass fractions, low aerosol hygroscopicity and high externally mixed weakly CCN‐active particles under low PBL height (< $< $100 m) indicated the influence of PBL on aerosol aging processes. In contrast, aerosol properties did not depend strongly on air mass origins or wind direction, implying that the observed aerosol and CCN are from local emissions. An error function could parameterize the relationship between CCN number and supersaturation throughout the campaign. Key Points: Planetary boundary layer height (HBL) is the major driving force of aerosol accumulation and aging processes in Delhi during late winterLower aerosol hygroscopicity and greater external mixing under low HBLIncreased CCN number concentration under low HBL due to higher aerosol concentration [ABSTRACT FROM AUTHOR]

With rising anthropogenic activities, surface ozone levels have increased across different parts of the world including India. Previous studies have shown that surface ozone shows distinct characteristics across India but these results are based on isolated locations and any comprehensive and spatiotemporally consistent study about surface ozone variability lacks thus far. Keeping these facts in mind, we utilize ground-based observations and reanalysis datasets to investigate spatiotemporal variations of surface ozone and its linkages with meteorology and precursors over Indian region. A validation exercise shows that the Copernicus Atmosphere Monitoring Service Reanalysis (CAMSRA) reasonably compares against the ground-based observations showing better correlations (> 0.7) over southern regions and relatively lesser (> 0.5) correlations over northern and eastern regions. We have further quantified this agreement in terms of range, mean absolute error (MAE), and root mean square error (RMSE). A time series analysis shows that the CAMSRA captures seasonal variations irrespective of location. Spatial distribution of surface ozone shows higher (lower) concentrations of about 40–60 ppb (15–20 ppb) during pre-monsoon (monsoon) months over northern and western parts and peninsular India. A prominent increase during May is noted over the northern region, especially over the Indo-Gangetic Plains (IGP). These seasonal variations are linked to solar radiation (SR), temperature, low-level circulation, and boundary layer height (BLH). CAMSRA-based surface ozone shows increasing trends across all four regions (north, east, west, and south India) and also India as a whole (0.069 ppb year−1, p = 0.001) with highest trends over the eastern region. Furthermore, principal component analysis (PCA) reveals that the first (second) mode shows a high percentage variance explained, ranging between 30 and 50% (10–20%). The corresponding PC-1 time series exhibits a notable increase in the surface ozone over south and central India, which corroborates the trend obtained through the area averaged time series. The second mode (PC-2) indicates prominent interannual variability over the IGP (southern India) in the pre-monsoon (post-monsoon). During the monsoon season, an interesting dipole pattern is noticeable, which closely resembles the active and break spell patterns of the Indian summer monsoon. Further, we quantify the weightage of precursors and meteorological parameters on surface ozone concentrations. The analysis suggests that PC1 of surface ozone is strongly influenced by CO and NOx (the precursors) while meteorology seems to dominate the PC2 during the pre-monsoon season. Overall, the results indicate that changes in the precursors or meteorological conditions have significant influences on the surface ozone concentrations across India. [ABSTRACT FROM AUTHOR]

Despite the importance of tropical ecosystems for climate regulation, biodiversity, water and nutrient cycles, only a few Critical Zone Observatories (CZOs) are located in the tropics. Among these, most are in humid climates, while very few data exist for semi‐arid and sub‐humid climates, due to the difficulty of estimating hydro‐geochemical balances in catchments with ephemeral streams. We contribute to fill this gap by presenting a meteorological and hydro‐geochemical dataset acquired at the Mule Hole catchment (4.1 km2), a pristine dry deciduous forest located in a biosphere reserve in south India. The dataset consists of time series of variables related to (i) meteorology, including rainfall, air temperature, relative humidity, wind speed and direction, and global radiation, (ii) hydrology, including water level and discharge at the catchment outlet, (iii) hydrogeology, including manual (monthly) and/or automated (from 15 min to hourly) groundwater levels in nine piezometers and (iv) geochemistry, including suspended sediment content in the stream and chemical composition of rainfall (event based), groundwater (monthly sampling) and stream water (storm events, 15 min to hourly frequency with an automatic sampler). The time series extend from 2003 to 2019. Measurement errors are minimized by frequent calibration of sensors and quality checks, both in the field and in the laboratory. Despite these precautions, several data gaps exist, due to occasional access restriction to the site and instrument destruction by wildlife. Results show that large seasonal and interannual variations of climatic conditions were reflected in the large variations of stream flow and groundwater recharge, as well as in water chemical composition. Notably, they reveal a long‐term evolution of groundwater storage, suggesting hydrogeological cycles on a decadal scale. This dataset, alone or in combination with other data, has already allowed to better understand water and element cycling in tropical dry forests, and the role of forest diversity on biogeochemical cycles. As tropical ecosystems are underrepresented by Critical Zone Observatories, we expect this data note to be valuable for the global scientific community. [ABSTRACT FROM AUTHOR]

Several ambient air quality records corroborate the severe and persistent degradation of air quality over northern India during the winter months, with evidence of a continued, increasing trend of pollution across the Indo-Gangetic Plain (IGP) over the past decade. A combination of atmospheric dynamics and uncertain emissions, including the post-monsoon agricultural stubble burning, make it challenging to resolve the role of each individual factor. Here we demonstrate the potential use of an atmospheric transport model, the Weather Research and Forecasting model coupled with chemistry (WRF–Chem) to identify and quantify the role of transport mechanisms and emissions on the occurrence of the pollution events. The investigation is based on the use of carbon monoxide (CO) observations from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5 Precursor satellite and the surface measurement network, as well as the WRF–Chem simulations, to investigate the factors contributing to CO enhancement over India during November 2018. We show that the simulated column-averaged dry air mole fraction (XCO) is largely consistent with TROPOMI observations, with a spatial correlation coefficient of 0.87. The surface-level CO concentrations show larger sensitivities to boundary layer dynamics, wind speed, and diverging source regions, leading to a complex concentration pattern and reducing the observation-model agreement with a correlation coefficient ranging from 0.41 to 0.60 for measurement locations across the IGP. We find that daily satellite observations can provide a first-order inference of the CO transport pathways during the enhanced burning period, and this transport pattern is reproduced well in the model. By using the observations and employing the model at a comparable resolution, we confirm the significant role of atmospheric dynamics and residential, industrial, and commercial emissions in the production of the exorbitant level of air pollutants in northern India. We find that biomass burning plays only a minimal role in both column and surface enhancements of CO, except for the state of Punjab during the high pollution episodes. While the model reproduces observations reasonably well, a better understanding of the factors controlling the model uncertainties is essential for relating the observed concentrations to the underlying emissions. Overall, our study emphasizes the importance of undertaking rigorous policy measures, mainly focusing on reducing residential, commercial, and industrial emissions in addition to actions already underway in the agricultural sectors. [ABSTRACT FROM AUTHOR]

The high-resolution spatio-temporal monitoring through low-cost sensors (LCS) provides important insights into the dynamics of localized pollution patterns and in conceptualizing effective environmental management and public health interventions. We assessed the role of meteorology and local sources on spatially distributed PM 2.5 pollutants measured from a unique urban-rural scaled network of LCS in Lucknow, India. The city-wide average PM 2.5 annual cycle ranged between 29 ± 7 μg/m3 during the monsoon to 64 ± 30 μg/m3 during the rest of the year. During non-monsoon seasons, >60% of air mass trajectories indicated regional level transportations primarily from the north-western plains of the Indo-Gangetic basin, with peak weighted concentration weighted trajectory (WCWT, scale: 0–100) value estimated at >70. To analyze the impact of local sources, we designed a statistical classification method to divide each seasonal distribution into five incremental concentration groups ranging from no risk zone to hotspots. The hotspots were identified both within urban and rural regions with their average concentration measured as 23 μg/m3 and 26 μg/m3 higher than the rest of the regions in pre-monsoon (2 hotspots) and post-monsoon (4 hotspots) season, respectively. Further analysis shows that the nighttime concentrations were much higher in several locations (up to 40% in pre-monsoon and 54% in post-monsoon compared to their daytime levels), indicating the greater impact of local sources in the presence of low boundary layer height. A diurnal trend analysis along with conditional bivariate probability function (CBPF) was performed to interpret the characteristics and locations of the dominant sources. The study highlights the importance of a dense network of LCS to scale air quality monitoring in spatially heterogeneous environments and as a futuristic tool for PM 2.5 exposure-based studies. [Display omitted] • Statistical classification method developed to analyze AQ data into pollution zones. • Long range transport is prominent from north-western areas in non-monsoon seasons. • Hotspot formation by localized sources can be identified during different seasons. • Nighttime traits with low PBLH offered ideal conditions for hyperlocal variations. • Rural hotspots exhibited distinct pollution dynamics and sources than urban areas. [ABSTRACT FROM AUTHOR]

This study aimed to assess the environmental risk and human health risks associated with PM 2. 5 -bound metals in Paradip city between January 2019 and December 2021. The seasonal average concentrations of PM 2.5 were measured 91.43 ± 70.18 μg m−3, 103.40 ± 60.80 μg m−3, 124.74 ± 62.37 μg m−3, and 159.37 ± 77.88 μg m−3 in pre-monsoon, monsoon, post-monsoon, and winter season respectively. The highest and lowest concentrations are estimated in the winter and pre-monsoon season. Paradip city experienced tropical weather conditions with a hot and humid climate. The wind pattern shows that the predominant wind direction was observed from the south-south-west (SSW) direction. The metals in PM 2.5 were analysed using an atomic absorption spectrophotometer (AAS) by air-acetylene flame using a hollow cathode lamp. The average metal concentration decreased in the order of Fe > Al > Zn > Pb > Cr > Mn > Ni > Cu > Co > Cd > As. The value of the geo-accumulation index (Igeo) was evaluated >1 for Cd, Fe, and Zn elements. The health risk assessment (HRA) results showed that non-carcinogenic risk (NCR) was higher through the inhalation route followed by ingestion and dermal contact. The cumulative NCR, which is expressed in terms of the hazard index (HI), is greater than 1 for infant (2.78E+00), child (2.53E+00), and adult (1.04E+00) via inhalation pathway. The total carcinogenic risk (TCR) for infants, children, and adults was estimated at 1.45E-04, 7.24E-05 and 1.25E-05, respectively, which exceeded the acceptable limit of 1.00E-06. Our comprehensive research plays an important role in both policymakers and relevant stakeholders for the preparation of city action plans concerning ambient air pollution, which can improve the air quality in and around Paradip city, India. [Display omitted] • The highest PM 2.5 and heavy metals concentrations were measured in the winter season. • The Contamination level of selected heavy metals was assessed by the Geo-accumulation Index (I geo). • Health risks associated with metals are assessed as non-carcinogenic and carcinogenic for infants, children, and adults. • The hazard index (HI) is greater than 1 for all age groups via the inhalation pathway. • Total cancer risks (TCR) were found above the acceptable limit of 1.00E-06. [ABSTRACT FROM AUTHOR]

The sensitive ecosystem of the central Himalayan (CH) region, which is experiencing enhanced stress from anthropogenic forcing, requires adequate atmospheric observations and an improved representation of the Himalaya in the models. However, the accuracy of atmospheric models remains limited in this region due to highly complex mountainous topography. This article delineates the effects of spatial resolution on the modeled meteorology and dynamics over the CH by utilizing the Weather Research and Forecasting (WRF) model extensively evaluated against the Ganges Valley Aerosol Experiment (GVAX) observations during the summer monsoon. The WRF simulation is performed over a domain (d01) encompassing northern India at 15 km × 15 km resolution and two nests (d02 at 5 km × 5 km and d03 at 1 km × 1 km) centered over the CH, with boundary conditions from the respective parent domains. WRF simulations reveal higher variability in meteorology, e.g., relative humidity (RH = 70.3 %–96.1 %) and wind speed (WS = 1.1–4.2 m s -1), compared to the ERA-Interim reanalysis (RH = 80.0 %–85.0 %, WS = 1.2–2.3 m s -1) over northern India owing to the higher resolution. WRF-simulated temporal evolution of meteorological variables is found to agree with balloon-borne measurements, with stronger correlations aloft (r = 0.44–0.92) than those in the lower troposphere (r = 0.18–0.48). The model overestimates temperature (warm bias by 2.8 ∘ C) and underestimates RH (dry bias by 6.4 %) at the surface in d01. Model results show a significant improvement in d03 (P = 827.6 hPa, T = 19.8 ∘ C, RH = 92.3 %), closer to the GVAX observations (P = 801.4 hPa, T = 19.5 ∘ C, RH = 94.7 %). Interpolating the output from the coarser domains (d01, d02) to the altitude of the station reduces the biases in pressure and temperature; however, it suppresses the diurnal variations, highlighting the importance of well-resolved terrain (d03). Temporal variations in near-surface P , T , and RH are also reproduced by WRF in d03 to an extent (r>0.5). A sensitivity simulation incorporating the feedback from the nested domain demonstrates the improvement in simulated P , T , and RH over the CH. Our study shows that the WRF model setup at finer spatial resolution can significantly reduce the biases in simulated meteorology, and such an improved representation of the CH can be adopted through domain feedback into regional-scale simulations. Interestingly, WRF simulates a dominant easterly wind component at 1 km × 1 km resolution (d03), which is missing in the coarse simulations; however, the frequency of southeasterlies remains underestimated. The model simulation implementing a high-resolution (3 s) topography input (SRTM) improved the prediction of wind directions; nevertheless, further improvements are required to better reproduce the observed local-scale dynamics over the CH. [ABSTRACT FROM AUTHOR]

Ground and satellite measurements of particulate pollution play an important role in determining the particulate pollutant-Aerosol Optical Depth (AOD) relationship. The daily observed PM10 and PM2.5 concentration varied from 11–757 μg/m3 and 8–630 μg/m3 with the mean concentrations of 137 ± 119 μg/m3 and 86 ± 90 μg/m3, respectively. The long-term mean annual PM10 and PM2.5 levels are several times higher than the WHO permissible limits. The 1377 satellite-derived AOD observations from the Moderate Resolution Imaging Spectrometer, ground-based particulate matter (PM) and meteorological observations from 2013–2017 were analysed to develop two-variate linear model (TVM) (AOD versus PM10 or PM2.5) and multi-variate regression models (MVMs) (AOD + meteorological parameters versus PM10 or PM2.5) for estimation of the ground level PM10 and PM2.5 in the Kashmir Himalaya, India. The model evaluation showed that the PM predication estimates are significant at 99% confidence level for all the models. The TVM predicts daily PM10 concentration better than PM2.5 explaining 82% and 74% variance in the observed data, respectively. By adding meteorological data to the regression analysis, there is an improvement of 5% and 11% in R2 for PM10 and PM2.5 estimates which inter alia reduced the RMSE by 11.8% and 20.47%, respectively. Estimation of the particulate pollution, utilising satellite-based AOD, observed PM and meteorology, would encourage satellite-based air quality monitoring in the data-scarce Himalaya. However, it is suggested that more studies are required to improve the operational prediction of PM pollution by incorporating satellite observations of other pollutants, and processes in the model using advanced approaches. [ABSTRACT FROM AUTHOR]

The article describes the unique weather phenomena that occurred in SINOBAS during the months of March, April, and May 2023. In March, there was a deficit of precipitation in the Peninsula and the archipelagos, while in April, most of the Peninsula experienced extreme drought. In May, there were storms and heavy precipitation in several regions. During this quarter, 26 events related to convective phenomena such as tornadoes, waterspouts, hailstorms, and torrential rainfall were recorded. SINOBAS users collaborated in inputting information into the system. [Extracted from the article]

Uncertainty and uneven distribution of monsoonal rainfall and its consequences on crop production is a matter of serious concern in India, specifically, in the Indo-Gangetic plain region. In this study, drought patterns were investigated through standardised precipitation index (SPI) of varying timescales, using the India Meteorological Department (IMD) precipitation data (1901-2021). We analysed the spatio-temporal pattern of different drought characteristics (frequency, duration, severity, intensity) of the Indian Gangetic basin using run theory. The bivariate copula method has been incorporated to combine two drought properties (severity and duration). Copula integrates multivariate distribution and considers the dependency rate among the variables. The five most widely used copulas from various copula families, elliptical (normal, t-copula) and Archimedean (Clayton, Gumbel, Frank), were estimated for modelling, and the best fit copula was selected. The study revealed that seasonal drought is more frequent and intense in the Upper and Middle Gangetic Plain, whereas annual drought is quite scattered in nature. It is worthy to mention that downward drought trends were observed in this agricultural belts significantly after 1965; specifically, in the Upper, Middle, and Trans Gangetic Plain regions. With increasing drought duration and severity, the drought return period raised, but the frequency decreased gradually. Most of the droughts characterised by less duration and severity occurred with a return period below 10 years for the whole region. The major 100 + years return period droughts were to be found after 1960 and their frequencies were significantly higher after 2000. The most recent remarkable droughts with more than 100 years of return occurred during 2008-2011 and 2016-2018 in the Upper and Middle Gangetic plains, whereas in the Lower Gangetic plain, a hundred-year return period drought was occurred during 2010-2013. This study provides agroclimatic-zones-wise significant information of drought characteristics and its nature of occurrence in the Indian Ganga Basin. The results enhance the understanding of drought management and formulation of adaptive strategies to mitigate the adverse impact of droughts., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Present study examines how ozone concentration changed under heatwave (HW) condition with emphasis on meteorological parameters in respect to non-heatwave (NHW) days. In this perspective, Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) has been used to simulate the surface O 3 (SfO 3 ) and maximum temperature (T max ) during NHW (11 th -19 th May 2015) and HW days (21 st -29 th May 2015) over southeast (SE), India. The WRF-Chem simulated meteorological and chemical variables have been evaluated against the ERA5 and CAMS reanalysis dataset. A significant correlation of 55-95% is found for all the meteorological and chemical variables. The influencing parameters shows positive correlation of ozone with temperature, which reaches 75-78 ppbv under HW condition. Day to day trend analysis reveal an increasing pattern of maximum temperature and SfO 3 concentration under HW condition. During HW, mixing of ozone-rich air aloft with near-surface air leading a rise in SfO 3 , as indicated by both ERA5 (with a maximum Planetary Boundary Layer Height (PBLH) of 1000 m) and WRF-Chem simulations (1600 m). Furthermore, the diurnal cycle of SfO 3 , temperature, PBLH reaches a peak at afternoon, while the other variables like nitrogen oxides (NO x ), Relative Humidity (RH) shows a high concentration at night-time. Overall, WRF-Chem model effectively captures the diurnal fluctuations of SfO 3 , NO x and the meteorological variables during the HW event over the SE, India. Result shows that HW may cause a strong contribution to the rate of increase in SfO 3 (22.17%). Thus, it is required to consider contribution of HW driven ozone when developing long-term strategies to mitigate regional ozone pollution., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

This study analyzes the air pollution characteristics and their relation to meteorological conditions in Chennai, India. Meteorological conditions were the primary factor determining variations in daily average pollutant concentrations. The influence of urban infrastructure on meteorology is an important prediction on air quality. Understanding of the seasonal and diurnal secondary pollutant concentrations as a function of local meteorological conditions is necessary for urban air quality management. Micro-scale models for analyzing the surface layer interactions with the surrounding environment have recently gained attention. An attempt has been made to understand the effect of meteorology on air quality. This comprehensive study aims to assess the influence of local meteorology on urban air quality. The correlation was established between the change in meteorological parameters and mixing height on air quality at selected locations in a tropical urban environment. Results indicated the significant impact of land use patterns on the dispersion of air quality at study locations. Seasonal variations of ambient air temperatures at study locations were found to be more than 3°C in summer. Average mixing height variation among the study locations was observed to be more than 200 meters in summer. Results indicated the importance of wind velocity on the mixing height at study locations. The average concentrations of air quality parameters showed significant variation among the study locations. The maximum ozone (O3) concentration was recorded at the Central Business District (CBD) during the afternoon, i.e., around 38.3 ppb, whereas it was 26.8 and 14.6 ppb at the Residential Area (RA) and Urban Baseline (UBL), respectively. A strong correlation was observed between ambient temperature and O3 concentration during summer. In the winter, the average O3 concentration in all three-study locations increased to 45.3 ppb, 45.8 ppb, and 58.5 ppb at UBL, RA, and CBD sites, respectively. The study reveals the impact of microenvironments on air quality. Implications: An attempt has been made to study the seasonal and diurnal variation of air quality levels in selected study regions with land cover change. This article focuses mainly on the surface temperature intensity variations with respect to the percentage of land use pattern change in Chennai city, India, and the subsequent effect on meteorology of dispersion conditions and air quality parameters has been studied. The relationship between local meteorology and air quality has been established. [ABSTRACT FROM AUTHOR]

Delineation of homogeneous reference evapotranspiration (ET0) regions is essential for different applications in hydro‐meteorology. In conventional regionalization approaches, lumped (time‐invariant) statistics such as mean, median or interquartile range of different hydrometeorological variables are often considered as attributes to delineate regions. Information on temporal dynamics of those variables is not utilized (as it is lost in lumped statistics), which if accounted could yield better regions. To address this, a new regionalization approach is presented in fuzzy framework in this Part 1 of a two‐part series. In the proposed approach, information on temporal dynamics of predictor climate variables influencing ET0 is used for regionalization, and the delineated regions are subsequently validated for homogeneity using the predictand (ET0) related information. Effectiveness of the approach is demonstrated through a case study on India, which yielded 18 regions. They are shown to be statistically more homogeneous in ET0 when compared to the existing agro‐ecological zones and regions formed using global fuzzy c‐means clustering method. The homogeneous ET0 regions were found to be different from homogeneous actual evapotranspiration (ETa) regions delineated over India using the fuzzy dynamic clustering approach. Various applications of the homogeneous ET0 regions formed using the proposed approach are presented in Part 2 of this series. [ABSTRACT FROM AUTHOR]

The first documented meteorological research in India was carried out by the British in the eighteenth century. Who, then, was the first native Indian to publish a paper on meteorology in an Indian journal? A paper entitled Note on a whirlwind at Pundooah by Chunder Sikur Chatterjee, included in the Proceedings of the Asiatic Society of Bengal in 1865, was probably the first paper published by a native Indian in an Indian journal, indicating that meteorology, as a subject of academic study, has deep roots in India. The paper was in the form of note reporting a tornado at Pundooah to the Surveyor General's Office of India. The note was unique in that it was supported by a sketch that meticulously depicted the path of a tornado and its direction of rotation, and detailed the horizontal extent of the suction vortex and tornado cyclone, which were determined from observations of the trail of destruction. The spatial and temporal scales of the tornado recorded in the note matched those given in papers published more than 100 years later by Orlansky and Fujita, and it is possible that it constitutes the first meteorological record in which the horizontal scale of a tornado and its suction spot were accurately evaluated. The paper was the first of its kind in which tornado dynamics were ascertained via observations. [ABSTRACT FROM AUTHOR]

A regional fully coupled meteorology–chemistry model, Weather Research and Forecasting model with Chemistry (WRF-Chem), was employed to study the seasonality of ozone (O3) pollution and its sources in both China and India. Observations and model results suggest that O3 in the North China Plain (NCP), Yangtze River Delta (YRD), Pearl River Delta (PRD), and India exhibit distinctive seasonal features, which are linked to the influence of summer monsoons. Through a factor separation approach, we examined the sensitivity of O3 to individual anthropogenic, biogenic, and biomass burning emissions. We found that summer O3 formation in China is more sensitive to industrial and biogenic sources than to other source sectors, while the transportation and biogenic sources are more important in all seasons for India. Tagged simulations suggest that local sources play an important role in the formation of the summer O3 peak in the NCP, but sources from Northwest China should not be neglected to control summer O3 in the NCP. For the YRD region, prevailing winds and cleaner air from the ocean in summer lead to reduced transport from polluted regions, and the major source region in addition to local sources is Southeast China. For the PRD region, the upwind region is replaced by contributions from polluted PRD as autumn approaches, leading to an autumn peak. The major upwind regions in autumn for the PRD are YRD (11 %) and Southeast China (10 %). For India, sources in North India are more important than sources in the south. These analyses emphasize the relative importance of source sectors and regions as they change with seasons, providing important implications for O3 control strategies. [ABSTRACT FROM AUTHOR]

The sensitive and fragile ecosystem of the central Himalayan (CH) region, experiencing enhanced anthropogenic pressure, requires adequate atmospheric observations and an improved representation of Himalaya in the models. However, the accuracies of atmospheric models remain limited here due to highly complex mountainous topography. This article delineates the effects of spatial resolution on the modeled meteorology and dynamics over the CH by combining the WRF (Weather Research and Forecasting) model with the GVAX (Ganges Valley Aerosol Experiment) observations during the summer monsoon. WRF simulation is performed over a domain (d01) encompassing northern India at 15 km × 15 km resolution, and two nests: d02 (5 km × 5 km) and d03 (1 km × 1 km) centered over CH with boundary conditions from respective parent domains. WRF simulations reveal higher variability in meteorology e.g. Relative Humidity (RH = 71.4-93.3 %), Wind speed (WS = 1.6-3.1 ms-1), as compared to the ERA Interim reanalysis (RH = 79.4-85.0, and WS = 1.3-2.3 ms-1) over the northern India owing to higher resolution. WRF simulated temporal evolution of meteorological profiles is seen to be in agreement with the balloon-borne measurements with stronger correlations aloft (r = 0.44-0.92), than those in the lower troposphere (r = 0.27-0.48). However, the model overestimates temperature (warm bias by 2.8 °C) and underestimates RH (dry bias by 7.6 %) at surface in the d01. Model results show a significant improvement in d03 (P = 827.6 hPa, T = 19.8 °C, RH = 90.2 %) and are closer to the GVAX observations (P = 801.3, T = 19.5, RH = 94.5 %). Temporal variations in near surface P, T and RH are also reproduced by WRF d03 to an extent (r > 0.5). A sensitivity simulation incorporating the feedback from nested domain demonstrated improvements in simulated P, T and RH over CH. Our study shows the WRF model set up at finer spatial resolution can significantly reduce the biases in simulated meteorology and such an improved representation of CH can be adopted through domain feedback into regional-scale simulations. Interestingly, WRF simulates a dominant easterly wind component at 1 km × 1 km resolution (d03), which was missing in the coarse simulations; however, a frequent southeastward wind component remained underestimated. Model simulation implementing a high resolution (3 s) topography input (SRTM) improved the prediction of wind directions, nevertheless, further improvements are required to better reproduce the observed local-scale dynamics over the CH. [ABSTRACT FROM AUTHOR]

The spatial coverage of PM 2.5 monitoring is non-uniform across India due to the limited number of ground monitoring stations. Alternatively, Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), is an atmospheric reanalysis data used for estimating PM 2.5 . MERRA-2 does not explicitly measure PM 2.5 but rather follows an empirical model. MERRA-2 data were spatiotemporally collocated with ground observation for validation across India. Significant underestimation in MERRA-2 prediction of PM 2.5 was observed over many monitoring stations ranging from -20 to 60 μg m -3 . The utility of Machine Learning (ML) models to overcome this challenge was assessed. MERRA-2 aerosol and meteorological parameters were the input features used to train and test the individual ML models and compare them with the stacking technique. Initially, with 10% of randomly selected data, individual model performance was assessed to identify the best model. XGBoost (XGB) was the best model (r 2  = 0.73) compared to Random Forest (RF) and LightGBM (LGBM). Stacking was then applied by keeping XGB as a meta-regressor. Stacked model results (r 2  = 0.77) outperformed the best standalone estimate of XGB. Stacking technique was used to predict hourly and daily PM 2.5 in different regions across India and each monitoring station. The eastern region exhibited the best hourly prediction (r 2  = 0.80) and substantial reduction in Mean Bias (MB = -0.03 μg m -3 ), followed by the northern region (r 2  = 0.63 and MB = -0.10 μg m -3 ), which showed better output due to the frequent observation of PM 2.5 >100 μg m -3 . Due to sparse data availability to train the ML models, the lowest performance was for the central region (r 2  = 0.46 and MB = -0.60 μg m -3 ). Overall, India's PM 2.5 prediction was good on an hourly basis compared to a daily basis using the ML stacking technique., Competing Interests: Declaration of competing interest The authors have no conflict of interest to declare., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

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 PM 2.5 /PM 10 variation revealed that the accumulation effect of relative humidity on PM 2.5 was more intense than PM 10 . 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., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

The present study focused on the temporal variability, meteorological influences, potential sources, and long-range transport of atmospheric aerosols over two contrasting environments during 2011-2013. We have chosen Agartala (AGR) city in Northeast India as one of our sites representing the rural-continental environment and Patiala (PTA) as an urban site in Northwest India. The seasonal averaged equivalent black carbon (eBC) concentration in AGR ranges from 1.55 to 38.11 µg/m 3 with an average value of 9.87 ± 8.17 µg/m 3 , whereas, at an urban location, PTA value ranges from 1.30 to 15.57 µg/m 3 with an average value of 7.83 ± 3.51 µg/m 3 . The annual average eBC concentration over AGR was observed to be ~ 3 times higher than PTA. Two diurnal peaks (morning and evening) in eBC have been observed at both sites but were observed to be more prominent at AGR than at PTA. Spectral aerosol optical depth (AOD) has been observed to be in the range from 0.33 ± 0.09 (post-monsoon) to 0.85 ± 0.22 (winter) at AGR and 0.47 ± 0.04 (pre-monsoon) to 0.74 ± 0.09 (post-monsoon) at PTA. The concentration of eBC and its diurnal and seasonal variation indicates the primary sources of eBC as local sources, synoptic meteorology, planetary boundary layer (PBL) dynamics, and distant transportation of aerosols. The wintertime higher values of eBC at AGR than at PTA are linked with the transportation of eBC from the Indo-Gangetic Plain (IGP). Furthermore, it is evident that eBC aerosols are transported from local and regional sources, which is supported by concentration-weighted trajectory (CWT) analysis results., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The nocturnal, seasonal and intra-annual variation of vertical distribution of tropospheric aerosols over two nearby stations Gadanki (13.5oN, 79.2oE) and Tirupati (13.6oN, 79.4oE) is investigated using ground-based Micro Pulse Lidar (MPL) and space-borne Lidar (CALIPSO) systems during 2010–2017. The nocturnal variation of aerosol extinction (AE) coefficient reveal high AE below ∼2 km in midnight hours and aerosols are slowly descending towards the surface during early morning hours. From the seasonal variation, AE values are found to be higher at lower altitudes (<2 km) during winter and post-monsoon seasons, a sharp decrease with increasing altitude is found in tandem with boundary layer and low wind speeds. Interestingly, during monsoon season, significant aerosol loading is found in the altitude range of ∼2–5.5 km mainly due to the influence of strong Low Level Jet (LLJ). The clean environment observed below ∼2 km during this season is attributed to the wet scavenging, downward vertical winds and existance of no strong local source. The seasonal mean AE profile derived from CALIPSO matches well with the MPL in all the seasons except in monsoon season where a large bias is noticed below 2 km. The intra-annual variation revealed more than 80% of aerosols existing above (below) the boundary layer during monsoon (winter) months contribute to the total Aerosol Optical Depth (AOD). The depolarization ratio (>0.2) in the month of July shows the dominance of dust particles which includes long-range transport over this locations. Back trajectories reveals that potential sources are changing from season to season at different altitudes and confirms that the aerosols observed at higher altitudes are advected from other land and oceanic regions. Thus, aerosol vertical distribution is mainly controlled by meteorology and dynamics over this region. Further, the reasonably good correlation found between MPL and MODIS AODs suggests that MODIS could provide reliable AOD over land region also. • Lidar observed aerosol vertical distribution over Indian tropical stations show strong nocturnal and seasonal variation. • Aerosol extinction over Gadanki/Tirupati region is higher at altitudes <2 km during winter and post-monsoon seasons. • Significant aerosol loading is noticed around ∼2–5.5 km and clean environment below ∼2 km during monsoon. • The contribution of aerosols above (below) the boundary layer during monsoon (winter) is found to be around 80% to the total AOD. • Back trajectory analyses confirms the advection of aerosols from other land and oceanic regions to the observation site. [ABSTRACT FROM AUTHOR]

Accurate representation of initial and boundary conditions of chemical species in numerical models is a major challenge for air quality prediction. Model for Ozone and Related chemical Tracers, version 4 (MOZART-4) is an offline global chemical transport model simulating various gases and particulates in the atmosphere. In this work, MOZART-4 analysis is used to modify the chemical boundary conditions over India to improve the forecast in an online-coupled model. A sensitivity study has been performed using the Weather Research and Forecasting (WRF) model coupled with Chemistry model (WRF-Chem) for the highly polluted winter month of December 2016. Daily two parallel experiments are performed from the WRF-Chem model with and without updating chemical initial and lateral boundary conditions using MOZART-4 analyses. Results show that the tropospheric ozone (O 3) and surface carbon monoxide (CO) forecasts are improved by 6% and 20%, respectively when MOZART-4 analysis is used to update chemical initial and lateral boundary conditions. Moreover, prediction of O 3 and CO is also improved vertically (reduction in root mean square difference by 58% and 26%, respectively) in different forecast lengths. The percentage improvement in surface CO, particulate matter of size less than 2.5 μm (PM 2.5) and particulate matter of size less than 10 μm (PM 10) forecasts is ∼22%, ∼4.3% and ∼20%, respectively when compared against ground observations from Central Pollution Control Board (CPCB), New Delhi stations. Overall, we observed improvement in O 3 , CO, PM 2.5 and PM 10 forecast using WRF-Chem model after initialization of chemical conditions from MOZART-4 analysis. [ABSTRACT FROM AUTHOR]

This is the first study to characterize the variation and emission of C 2 -C 5 non-methane volatile organic compounds (NMVOCs) in a semi-urban site of western India based on measurements during February–December 2015. Anthropogenic NMVOCs show clear seasonal dependence with highest in winter and lowest in monsoon season. Biogenic NMVOCs likes isoprene show highest mixing ratios in the pre-monsoon season. The diurnal variation of NMVOC species can be described by elevated values from night till morning and lower values in the afternoon hours. The elevated levels of NMVOCs during night and early morning hours were caused mainly by weaker winds, temperature inversion and reduced chemical loss. The correlations between NMVOCs, CO and NOx indicate the dominant role of various local emission sources. Use and leakage of liquefied petroleum gas (LPG) contributed to the elevated levels of propane and butanes. Mixing ratios of ethylene, propylene, CO, NOx, etc. show predominant emissions from combustion of fuels in automobiles and industries. The Positive Matrix Factorization (PMF) source apportionments were performed for the seven major emission sectors (i.e. Vehicular exhaust, Mixed industrial emissions, Biomass/Fired brick kilns/Bio-fuel, Petrochem, LPG, Gas evaporation, Biogenic). Emissions from vehicle exhaust and industry-related sources contributed to about 19% and 40% of the NMVOCs, respectively. And the rest (41%) was attributed to the emissions from biogenic sources, LPG, gasoline evaporation and biomass burning. Diurnal and seasonal variations of NMVOCs were controlled by local emissions, meteorology, OH concentrations, long-range transport and planetary boundary layer height. This study provides a good reference for framing environmental policies to improve the air quality in western region of India. Pie chart indicating the source profiles using PMF analysis at an urban region of Udaipur, India. Image 1053 • First continuous measurements of NMVOCs, CO and NOx in semi urban region of India. • Role of biomass burning and biogenic emissions in diurnal and seasonal variations. • Estimation of characteristic emission ratios of NMVOCs for sources. • PMF model highlights major emissions from vehicle exhaust and industrial sources. • A reference for framing air quality policies in western regions of India. [ABSTRACT FROM AUTHOR]

Artificial Neural Networks (ANNs) have been used increasingly in recent years for numerous hydrological applications because of their ability to model the non-linear relationships. The major drawback of ANNs is that little is known about what is happening inside the ANN resulting into designating them as 'black box' models. Since long researchers have been trying to extract the knowledge from the trained ANNs, which will help them to become more widely accepted and reach their full potential as hydrological models. There were few such attempts in hydrology particularly for ANN model developed for river flow forecasting and rainfall–runoff process. The prime focus of this paper is to extract knowledge locked in the weights and biases of the trained ANN models using a new method proposed by the authors. For this, ANN models were developed to estimate pan evaporation at three stations in India using meteorological variables. The proposed method was able to throw a light on working of ANN and its understanding of physics in that it could correctly evaluate the influence of the input variables on the evaporation as directly or inversely proportional which was endorsed by the physics of the underlying process. [ABSTRACT FROM AUTHOR]

The feedback between ambient aerosols and planetary boundary layer (PBL) meteorology has been proven to play a critical role in the enhancement of haze pollution. Vertical distribution of aerosols as well as temperature stratification are vital to understand aerosol – boundary layer interaction (ABI) and its impact on air quality deterioration. In current regional air quality models, the default vertical grid setting is relatively coarse and decreases progressively with altitude. However, the ABI is sensitive to aerosol layer at specific altitudes, i.e. around the top of PBL. This work aims to explore optimized vertical grid setting for better characterizing ABI and its role in air quality degradation. A single column model (SCM) is used for sensitivity tests considering the balance between model performance and computational cost. This optimized grid setting is then applied in three-dimensional air quality modelling in eastern China. Compared with default configuration, the optimized one is demonstrated to perform much better in characterizing temperature stratification and extreme fine particle (PM 2.5) concentration as well as its diurnal variation during haze episodes. Specifically, the averaged decrease in PBL height and increment in surface PM 2.5 concentration are about 5% and 20% while applying optimized setting, thereby reducing the mean bias from 30 to 5 μg/m3 in PM 2.5 concentration. The improvements could be attributed mainly to more accurate profiles of aerosol and its heating effect, and thus stabilized lower atmosphere. The optimization of vertical grid setting could be applied to areas with high concentration of absorbing aerosols such as eastern China and India, which could help better predict extreme near-surface pollution episodes. • The upper PBL and near surface layer are identified as two critical levels in aerosol-boundary layer interaction. • A key level-targeted vertical grid setting with more layers putted at two critical levels was proposed. • The optimized resolution performs better in characterizing extreme PM 2.5 concentration as well as its diurnal variation. [ABSTRACT FROM AUTHOR]

The rising number of extreme rainfall events over the Himalayan foothill states of India during the recent decades has become a serious issue with the growing concern of aerosol influences. This study intends to provide some insight into aerosol and gas chemistry responses to changes in monsoon circulation and precipitation, and also assess the impact of aerosols on two recent infamous heavy rainfall events using coupled meteorology–chemistry–aerosol (WRF-Chem) model simulations. The sensitivity of aerosols and chemistry on rainfall distribution and the amount is evaluated using the simulations with and without chemistry. Results from this study show that the magnitude and spatial distribution of precipitation are significantly influenced by including aerosol and gas chemistry in the model simulations. Realistic meteorological conditions as well as rainfall amount and distribution are reproduced when aerosols and gasses are taken into account in the simulation. There is an overall enhancement of total cumulative rainfall as high as 20% due to aerosols and gas chemistry over the western Himalayan Indian states. This study shows that cloud-microphysical properties and the resulting precipitation distribution depend critically on the aerosol types and their concentrations under similar thermodynamic conditions. This study highlights the role of aerosol and gas chemistry and recognizes the importance of atmospheric chemistry in the model simulation for the analysis of Himalayan extreme precipitation events, and its further associations with the Himalayan hydrology. [ABSTRACT FROM AUTHOR]

Introduction: Knowledge of species richness patterns and their relation with climate is required to develop various forest management actions including habitat management, biodiversity and risk assessment, restoration and ecosystem modelling. In practice, the pattern of the data might not be spatially constant and cannot be well addressed by ordinary least square (OLS) regression. This study uses GWR to deal with spatial non-stationarity and to identify the spatial correlation between the plant richness distribution and the climate variables (i.e., the temperature and precipitation) in a 1° grid in different biogeographic zones of India. Methodology: We utilized the species richness data collected using 0.04 ha nested quadrats in an Indian study. The data from this national study, titled ‘Biodiversity Characterization at Landscape Level’, were aggregated at the 1° grid level and adjudged for sampling sufficiency. The performances of OLS and GWR models were compared in terms of the coefficient of determination (R2) and the corrected Akaike Information Criterion (AICc). Results and discussion: A comparative study of the R2 and AICc values of the models showed that all the GWR models performed better compared with the analogous OLS models. The climate variables were found to significantly influence the distribution of plant richness in India. The minimum precipitation (Pmin) consistently dominated individually (R2 = 0.69; AICc = 2608) and in combinations. Among the shared models, the one with a combination of Pmin and Tmin had the best model fits (R2 = 0.72 and AICc = 2619), and variation partitioning revealed that the influence of these parameters on the species richness distribution was dominant in the arid and the semi-arid zones and in the Deccan peninsula zone. Conclusion: The shift in climate variables and their power to explain the species richness of biogeographic zones suggests that the climate–diversity relationships of plants species vary spatially. In particular, the dominant influence of Tmin and Pmin could be closely linked to the climate tolerance hypothesis (CTH). We found that the climate variables had a significant influence in defining species richness patterns in India; however, various other environmental and non-environmental (edaphic, topographic and anthropogenic) variables need to be integrated in the models to understand climate–species richness relationships better at a finer scale. [ABSTRACT FROM AUTHOR]

In cloud environment, big data analytics is a very innovative idea;Big data is defined as a very huge quantity of data. To extract meaning full value from it we need different technologies for analysis of the data. Big data Analytic is used to study pattern of historical data and the effect of changes of different parameters. Rainfall Forecasting has been a standout amongst the most intriguing and captivating space and it assumes a noteworthy part in meteorology and daily human life. Rainfall forecasting is used to estimate the probability of whether it will rain or not. Rainfall situation is the state of atmosphere at a given time in terms of different parameter like temperature, humidity and wind speed. Forecasting of rainfall is important to facilitate preparing for the finest and the nastiest of the climate. India is agrarian country so it is essential to know in advance about climate. This paper presents big data analytics for Rainfall forecasting and studies the advantage of using it. The algorithm which is used in this project is Time Series. The intention of time series analysis is generally in two ways, one is to recognize or representation the stochastic mechanisms that gives rise to an observed series and second is to predict or forecast the future value of an arrangement in light of the historical backdrop of that arrangement. Forecasts are prepared for new data when the actual result may not be known until some future date. What's to come is being anticipated, yet all earlier perceptions are quite often treated similarly. [ABSTRACT FROM AUTHOR]

The article elucidates the ideologies behind the colonial policy regarding the mitigation of earthquakes and cyclonic hazards in nineteenth- and twentieth-century India. Colonial encounters with the natural world of the Indian subcontinent had generated much discontent and uneasiness between the rulers and the ruled. There is no doubt that the environmental or natural policies of the colonial state were guided by economic interests, but in the cases of natural disasters like earthquakes and cyclones, these were unleashed in a more critical and dramatic way. The present article intends to critically examine the geological and cyclonological developments in colonial India as part of the disaster mitigation process and thereby explore the colonial attitude towards natural disasters. The economy and politics of disasters had evolved in the course of time in accordance with the shifting interests of colonial rulers. The article does not merely intend to deal with the 'science' of the disasters but delves into the historical evolution of geological and cyclonological study in colonial India. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ET0) plays a fundamental role in irrigated agriculture. The objective of this study is to simulate monthly ET0 at a meteorological station in India using a new method, an improved support vector machine (SVM) based on the cuckoo algorithm (CA), which is known as SVM-CA. Maximum temperature, minimum temperature, relative humidity, wind speed and sunshine hours were selected as inputs for the models used in the simulation. The results of the simulation using SVM-CA were compared with those from experimental models, genetic programming (GP), model tree (M5T) and the adaptive neuro-fuzzy inference system (ANFIS). The achieved results demonstrate that the proposed SVM-CA model is able to simulate ET0 more accurately than the GP, M5T and ANFIS models. Two major indicators, namely, root mean square error (RMSE) and mean absolute error (MAE), indicated that the SVM-CA outperformed the other methods with respective reductions of 5–15% and 5–17% compared with the GP model, 12–21% and 10–22% compared with the M5T model, and 7–15% and 5–18% compared with the ANFIS model, respectively. Therefore, the proposed SVM-CA model has high potential for accurate simulation of monthly ET0 values compared with the other models. [ABSTRACT FROM AUTHOR]

Background: Melioidosis is gaining recognition as an emerging infectious disease with diverse clinical manifestations and high-case fatality rates worldwide. However, the molecular epidemiology of the disease outside the endemic regions such as northeast part of Thailand and northern Australia remains unclear. Methodology/Principal findings: Clinical data and B. pseudomallei isolates obtained from 199 culture-confirmed cases of melioidosis diagnosed during 2006–2016 in South India were used to elucidate the host and pathogen specific variable virulence determinants associated with clinical presentations and disease outcome. Further, we determined the temporal variations and the influence of ecological factors on B.pseudomallei Lipopolysaccharide (LPS) genotypes causing infections. Severe forms of the disease were observed amongst 169 (85%) patients. Renal dysfunction and infection due to B.pseudomallei harboring BimABm variant had significant associations with severe forms of the disease. Diabetes mellitus, septicemic melioidosis and infection due to LPSB genotype were independent risk factors for mortality. LPSB (74%) and LPSA (20.6%) were the prevalent genotypes causing infections. Both genotypes demonstrated temporal variations and had significant correlations with rainfall and humidity. Conclusion/Significance: Our study findings suggest that the pathogen specific virulence traits under the influence of ecological factors are the key drivers for geographical variations in the molecular epidemiology of melioidosis. [ABSTRACT FROM AUTHOR]

Regional changes in surface meteorological variables are one of the key issues affecting the Indian subcontinent especially in recent decades. These changes impact agriculture, health, water, etc., hence important to assess and investigate these changes. The Indian subcontinent is characterized by heterogeneous temperature regimes at regional and seasonal scales. The India Meteorological Department (IMD) observations are limited to recent decades as far as its spatial distribution is concerned. In particular, over Hilly region, these observations are sporadic. Due to variable topography and heterogeneous land use/land cover, it is complex to substantiate impacts. The European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim (ERA-I) reanalysis not only covers a larger spatial domain but also provides a greater number of inputs than IMD. This study used ERA-I in conjunction with IMD gridded data to provide a comparative assessment of changing temperature patterns over India and its subregions at both regional and seasonal scales. Warming patterns are observed in both ERA-I and IMD data sets. Cold nights decrease during winter; warm days increase and warm spell duration increased during winter could become a cause of concern for society, agriculture, socio-economic reasons, and health. Increasing warm days over the hilly regions may affect the corresponding snow cover and thus river hydrology and glaciological dynamics. Such changes during monsoon are slower, which could be attributed to moisture availability to dampen the temperature changes. On investigation and comparison thereon, the present study provisions usages of ERA-I-based indices for various impact and adaptation studies. [ABSTRACT FROM AUTHOR]

Enhancing resilience of rural communities to climate change requires a clear understanding of micro-level perceptions and adaptation issues and their integration with the rural developmental framework. We collected household level data to understand grass-root perspectives on climate variability, impacts and barriers to adaptation in two different districts; Moga, Punjab and Mahbubnagar, Telangana. Further the study uses meteorological data to validate farmers perceptions. The results show that change in the quantum and distribution of rainfall, rising temperature, ground water depletion, lower farm income, higher unemployment and rural migration are some of the major impacts of climate change. Moreover, farmers perceptions on climate variability were consistent with the observed climate trend. Against climatic variations farmers were making shift to crop varieties of suitable duration, curtailing expenditure, borrowing and participating in employment guarantee schemes. However, farmers responses were constrained by barriers like lack of accessibility to weather information, limited knowledge on the cost-benefit of adaptation, inaccessibility to climate smart technologies, inadequate financial resources and unawareness on welfare schemes. The study concludes there is a need to reorient the developmental programmes at the macro-level considering micro-level needs and constraints for climate resilient agriculture. [ABSTRACT FROM AUTHOR]

Considering the significance of PM 1 aerosol in assessing health impacts of air pollution, an extensive analysis of PM 1 samples collected at an urban site in Delhi is presented in this study. Overall, PM 1 contributed to about 50 % of PM 2.5 mass which is alarming especially in Delhi where particle mass loadings are usually higher than prescribed limits. Major portion of PM 1 consisted of organic matter (OM) that formed nearly 47 % of PM 1 mass. Elemental carbon (EC) contributed to about 13 % of PM 1 mass, whereas SO 4 2- (16 %), NH 4 + (10 %), NO 3 - (4 %) and Cl - (3 %) were the major inorganic ions present. Sampling was performed in two distinctive campaign periods (in terms of meteorological conditions and heating (fire) activities), during the year 2019, each spanning two-week time, i.e. (i) September 3 rd -16 th (clean days), and (ii) November 22 nd -December 5 th (polluted days). Additionally, PM 2.5 and black carbon (BC) were measured simultaneously for subsequent analysis. The 24-h averaged mean concentrations of PM 2.5 and BC during clean days (polluted days) were 70.6 ± 26.9 and 3.9 ± 1.0 μg m -3 (196 ± 104 and 7.6 ± 4.1 μg m -3 ), respectively, which were systematically lower (higher) than that of the annual mean (taken from studies conducted at same site in 2019) of 142 and 5.7 μg m -3 , respectively. Changes in characteristic ratios (i.e., organic carbon (OC)/elemental carbon (EC) and K + /EC) of chemical species detected in PM 1 show an increase in biomass emissions during polluted days. Increase in biomass emission can be attributed to increase in heating practices (burning of biofuels such as wood logs, straw, and cow-dung cake) in- and around- Delhi because of fall in temperature during second campaign. Furthermore, a significant increase in NO 3 - fraction of PM 1 is observed during second campaign which shows fog processing of NO X due to conducive meteorological conditions in winters. Also, comparatively stronger correlation of NO 3 - with K + during second campaign (r = 0.98 as compared to r = 0.5 during first campaign) suggests the increased heating practices to be a contributing factor for increased fraction of NO 3 - in PM 1 . We observed that during polluted days, meteorological parameters such as dispersion rate also played a major role in intensifying the impact of increased local emissions due to heating activities. Apart from this, change in the direction of regional emission transport to study site and the topology of Delhi are the possible reasons for the elevated pollution level, especially PM 1 during winter in Delhi. This study also suggests that black carbon measurement techniques used in current study (optical absorbance with heated inlet and evolved carbon techniques) can be used as reference techniques to determine the site-specific calibration constant of optical photometers for urban aerosol., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Ten years (2008–2017) of Black Carbon (BC) observations obtained using Aethalometer (AE-31) are analyzed to investigate the seasonal trends and temporal variabilities over a tropical site Gadanki (13.5° N, 79.2° E) located in south-east India. Diurnal variations of BC have two peak structures one in the morning (∼08 IST) in all seasons and second in the evening (∼20 IST) only during the pre-monsoon (March–May). Intra-annual variation in BC indicated February and March months as the bio-mass burning with highest BC mass concentration (3000–5000 ng/m 3 ). About 46% of air parcel back trajectories found passing across the in-land regions of southern peninsular India brining transported aerosol to the source location during pre-monsoon. The lowest BC (∼1500 ng/m 3 ) is noticed during the monsoon months (June–September). The average BC (2200 ng/m 3 ) represents observational site as a typical rural site. The inter-annual variability of BC did not show any significant trend. However, trends in the maximum (March) and minimum (July) BC values show statistically significant decreasing trend suggesting reduction in bio-mass burning sources during March supported by the decrease in the fire counts. Diurnal variation in the absorption angstrom exponent indicates that the morning and evening peaks are contributed by the bio-mass combustion with values above threshold of 1. However, angstrom exponent values are found below 1 during noon time of monsoon season suggesting fossil fuel contribution. Strong coupling is found between aerosol concentration and tropospheric dynamics, meteorology in addition to the sources. The present study is expected to provide valuable input to the modelers and observational physicists as BC is climate sensitive variable. [ABSTRACT FROM AUTHOR]

The existence of elevated aerosol layer is common over India during monsoon season. Though its sources are well explained through long-range transport, its formation and maintenance is not explained to date. The formation and maintenances of an elevated aerosol layer, starting from ∼2 km and extending up to ∼5.5 km noticed is explained using two nearby lidars located in peninsular India. Existence of a cleaner environment with low aerosol loading below 2 km is attributed to the wet scavenging and existence of no strong local source. The low level jet (LLJ) from Arabian Sea persisting between 2 and 3 km is the main mechanism suggesting strong role of dynamics in the formation of these elevated layers. Persistent strong shears existing between LLJ and tropical easterly jet during this season restrict the up-liftment of aerosols to the higher altitudes. Observed features are explained in the light of dynamics, meteorology and long-range transport. [ABSTRACT FROM AUTHOR]

Particulate matter smaller than 2.5 μm (referred to as PM 2.5 ) is the most important criteria pollutant impacting human health, environment and climate. India is already recognized as pollution hotspot where PM 2.5 has been increasing in the recent past. Here we project anthropogenic PM 2.5 for the near future (till 2040) over India using multiple linear regression (MLR) approach based on RegCM projected meteorology and ECLIPSE projected emission. MISR derived PM 2.5 concentration (μg/m³) for the year 2010–2012 has been used to train the MLR model with reasonable accuracy (R > 0.9). The impact of the meteorological parameters under both RCP4.5 and 8.5 scenarios partially negates the impact of rising emission in future; more so in RCP8.5 than in RCP4.5 scenario. Air quality is projected to improve significantly with short lived climate pollutant (SLCP) 'mitigation' scenario in comparison with current legislation (CLE) 'baseline' emission scenario. Spatial analysis identifies a rapid increase in anthropogenic PM 2.5 in the eastern Indian states of Jharkhand, Chhattisgarh and Odisha, Peninsular India, and Delhi National Capital Region. Our results identify the near future pollution hotspots that would be useful in air quality management planning for the near future. [ABSTRACT FROM AUTHOR]

Northern India (23-31°N, 68-90°E) is one of the most densely populated and polluted regions in world. Accurately modeling pollution in the region is difficult due to the extreme conditions with respect to emissions, meteorology, and topography, but it is paramount in order to understand how future changes in emissions and climate may alter the region's pollution regime. We evaluate the ability of a developmental version of the new-generation NOAA GFDL Atmospheric Model, version 4 (AM4) to simulate observed wintertime fine particulate matter (PM2.5) and its relationship to meteorology over Northern India. We compare two simulations of GFDL-AM4 nudged to observed meteorology for the period 1980-2016 driven by pollutant emissions from two global inventories developed in support of the Coupled Model Intercomparison Project Phases 5 (CMIP5) and 6 (CMIP6), and compare results with ground-based observations from India's Central Pollution Control Board (CPCB) for the period 1 October 2015-31 March 2016. Overall, our results indicate that the simulation with CMIP6 emissions produces improved concentrations of pollutants over the region relative to the CMIP5-driven simulation. While the particulate concentrations simulated by AM4 are biased low overall, the model generally simulates the magnitude and daily variability of observed total PM2.5. Nitrate and organic matter are the primary components of PM2.5 over Northern India in the model. On the basis of correlations of the individual model components with total observed PM2.5 and correlations between the two simulations, meteorology is the primary driver of daily variability. The model correctly reproduces the shape and magnitude of the seasonal cycle of PM2.5, but the simulated diurnal cycle misses the early evening rise and secondary maximum found in the observations. Observed PM2.5 abundances are by far the highest within the densely populated Indo-Gangetic Plain, where they are closely related to boundary layer meteorology, specifically relative humidity, wind speed, boundary layer height, and inversion strength. The GFDL AM4 model reproduces the overall observed pollution gradient over Northern India as well as the strength of the meteorology-PM2.5 relationship in most locations. [ABSTRACT FROM AUTHOR]