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Chloride-rich particulate matter has been identified as a major contributor to air-quality deterioration in cities across India. Identification and reduction of chloride emissions could therefore improve visibility and human health across the region.

A detailed overview of long‐term secular trends in temperature of the mesosphere and lower thermosphere considered to be induced by increase in greenhouse gases has been provided by Beig et al. (2003). Since then, quite a few new results have been emerged as some of the data series have become sufficiently large enough to provide results with improved confidence. Our understanding on the nature of temperature trends in the mesosphere/lower thermosphere (MLT) region is relatively better now. In the mesosphere, some of the results confirmed the earlier findings, and some new results obtained by satellite and lidar data over the tropical region have indicated a relatively weaker cooling trend as compared to the past but nevertheless strengthened the conclusion about the cooling trends. However, in the mesopause region, some of the new results now indicate a break in trend and tendency of negative signal where earlier no trend feature was noticed. This slice of no trend feature in between two cooling regimes was puzzling the modeling community, who were in search of a convincing explanation. This paper briefly outlines the progress made over the recent past in the field of MLT region secular temperature trends attributed mainly to growth of greenhouse gases near the Earth's surface. Key Points Provides an update to an overview of anthropogenic signals Cooling in the mesosphere due to anthropogenic activities Break in secular trends near mesopause region

Understanding trends in any atmospheric quantity typically requires the ability to distinguish between naturally occurring processes that result in trends, such as the 11 year solar cycle, and potential anthropogenic secular trends that occur simultaneously. After the review of mesospheric and lower thermospheric temperature response to solar activity by Beig et al. (2008), a few new results along with some modified results by revisiting the older data sets have been reported recently. Main improvement is due to the length of data series and amount of data which have been accounted in recent years. This article summarizes the progress made in the field of temperature variability due to changing solar activity as reported recently. Recent investigations revealed that the solar signal becomes stronger with increasing latitude in the mesosphere. Temperature response to solar activity at the lower part of mesopause region is around a few degrees per 100 solar flux units (sfu), which becomes stronger (4–5 K/100 sfu) in the upper part of this region in both hemispheres. The overall global picture indicates that the solar signal in the mesopause region temperature in the Northern Hemisphere is relatively stronger in recent time in a majority of locations compared to results reported in earlier reviews. Key Points Temperature response to 11 year solar cycle in the mesospheric region How natural variability plays a role in secular trends Review of solar response in temperature of MLT region

Ground and satellite measurements of particulate pollution play an important role in determining the particulate pollutant-Aerosol Optical Depth (AOD) relationship. The daily observed PM 10 and PM 2.5 concentration varied from 11–757 μg/m 3 and 8–630 μg/m 3 with the mean concentrations of 137 ± 119 μg/m 3 and 86 ± 90 μg/m 3 , respectively. The long-term mean annual PM 10 and PM 2.5 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 PM 10 or PM 2.5 ) and multi-variate regression models (MVMs) (AOD + meteorological parameters versus PM 10 or PM 2.5 ) for estimation of the ground level PM 10 and PM 2.5 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 PM 10 concentration better than PM 2.5 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 R 2 for PM 10 and PM 2.5 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.

Particulate pollution of any area is not only influenced by its local sources, but meteorological parameters too govern its dispersion in the atmosphere, its concentration and deposition. The present study evaluates the role of seasonal meteorological factors in governing the concentration of particulate pollution in one of the most polluted cities in the world, Delhi, India. The work analyzed the particulate matter (PM) concentration at four selected sites of Delhi covering North, South, East and West Delhi. The influence of meteorological factors such as temperature, relative humidity and wind speed on the concentration of PM was evaluated using Pearson correlation statistics. Also, the effect of long-range dispersion on the PM concentration was studied by computing the backward HYSPLIT particle trajectory model. The outcome of the study depicted that North and East Delhi are the most polluted areas of Delhi and seasonal meteorology has a complex role in the PM concentration of the selected areas. Majorly in the summer and monsoon season, where dispersion and washing out effect prevails, temperature showed significant positive and relative humidity with a significant negative impact on PM concentration at all the selected sites. The long-range dispersion pattern too was influenced by season, as during the monsoon season wind coming to Delhi were southeasterly and in other seasons (summer, post-monsoon and winter) winds were predominantly from the northwestern part of India, which bring a lot of dust and smoke from the neighboring states that increase the PM concentration of Delhi. The study showed that it is not only the local sources within Delhi that make it the most polluted city in the world, but the climatic profile of this city also puts it in a vulnerable state by increasing its particulate matter concentration that comes from nearby areas.

We evaluate numerical simulations of surface ozone mixing ratios over the south Asian region during the pre-monsoon season, employing three different emission inventories in the Weather Research and Forecasting model with Chemistry (WRF-Chem) with the second-generation Regional Acid Deposition Model (RADM2) chemical mechanism: the Emissions Database for Global Atmospheric Research – Hemispheric Transport of Air Pollution (EDGAR-HTAP), the Intercontinental Chemical Transport Experiment phase B (INTEX-B) and the Southeast Asia Composition, Cloud, Climate Coupling Regional Study (SEAC4RS). Evaluation of diurnal variability in modelled ozone compared to observational data from 15 monitoring stations across south Asia shows the model ability to reproduce the clean, rural and polluted urban conditions over this region. In contrast to the diurnal average, the modelled ozone mixing ratios during noontime, i.e. hours of intense photochemistry (11:30–16:30 IST – Indian Standard Time – UTC +5:30), are found to differ among the three inventories. This suggests that evaluations of the modelled ozone limited to 24 h average are insufficient to assess uncertainties associated with ozone buildup. HTAP generally shows 10–30 ppbv higher noontime ozone mixing ratios than SEAC4RS and INTEX-B, especially over the north-west Indo-Gangetic Plain (IGP), central India and southern India. The HTAP simulation repeated with the alternative Model for Ozone and Related Chemical Tracers (MOZART) chemical mechanism showed even more strongly enhanced surface ozone mixing ratios due to vertical mixing of enhanced ozone that has been produced aloft. Our study indicates the need to also evaluate the O3 precursors across a network of stations and the development of high-resolution regional inventories for the anthropogenic emissions over south Asia accounting for year-to-year changes to further reduce uncertainties in modelled ozone over this region.

The outbreak of COVID-19, a global health challenge faced by countries worldwide, led to a lockdown in India, thereby bringing down the emissions of various air pollutants. Here, we discuss the behaviour of surface ozone (O 3 ) concentrations and its precursors, oxides of nitrogen (NO x ), carbon monoxide (CO), and volatile organic compounds (VOC) at two Indian megacities namely Mumbai and Pune, closely located yet vastly differing in meteorology due to their locations. Although levels of CO, NO 2 , and VOC declined sharply after the lockdown in both cities, with NO 2 showing the highest reduction, ozone concentration in Pune remained unaffected, whereas Mumbai exhibited a mixed trend, touching even a maximum in between the lockdown. On a diurnal scale, the magnitude of O 3 levels during the lockdown period is higher at almost all hours in Mumbai, and in Pune, it is almost identical except during night hours when it is marginally higher in the lockdown period as compared to the normal period. On a whole, the pollution levels were brought down significantly which can be used as a benchmark in the future for the implementation of policies related to air quality management and emission control in Indian megacities by the policymakers. These results also can pave a way for the scientific community for local air quality modelling.

This study presents the characteristics of ground level atmospheric ozone (O 3 ) over the rural mid-Brahmaputra Valley region of the northeastern India. Ozone and oxides of nitrogen (NO x  = NO + NO 2 ) concentration data were obtained from continuous measurement of O 3 and NO x housed at the MAPAN-AQM station at Tezpur University. The meteorological parameters were obtained from the same station. The diel, monthly, and seasonal variations of O 3 were studied. The O 3 -NO x photostationary state ( PS ) was carefully examined and it was found that the net O 3 concertation deviated substantially from the PS during the winter season. The deviation could be attributed to local biomass burning, biogenic VOC emission from forest and agriculture, and long-range transport of peroxyacyl nitrate (PAN). The long-range transport has been ascertained by examining the ventilation coefficients (VC), which correlated with the steep growth of net O 3 concentrations in the morning hours. The HYSPLIT air mass back trajectories were used in concentration-weighted trajectory (CWT) analyses of O 3 to assess the long-range regional transport of O 3 precursors, which positively influenced local O 3 concentrations.

The earth’s surface ozone levels are becoming very significant due to their negative impact on human health, vegetation and climate. In this study, the methodology based on ensemble approach embodied linear and nonlinear behaviors was developed. It was applied for prediction of ozone concentration using dataset (2013–2016) of gaseous pollutants (O 3 , CO, NO x , MHC, TNMHCs) and meteorological variables as input variables. The daily O 3 max/O 3 min ratio of 10.9 marks the peculiar ozone pollution in the area. The fourteen prediction algorithms and their possible combinations of ensemble models were employed in this paper. Compared with individual models, the ensemble model approach showed an index of agreement of 0.91, the accuracy of 95.5% and mean absolute error of − 0.001 ppb between the predicted and observed diurnal cycle and daily averaged data of the year 2016 for benchmark analysis.

In this study, we present a first-ever effort made to develop an ultra-high-resolution gridded emission inventory (i.e. ~ 0.4 km ×  ~ 0.4 km) for the Indian megacity Kolkata. As the rising demand for fossil fuels based energy along with the spread of urban corridors have forced the anthropogenic activities to a mounting level, therefore determining the sources responsible is of paramount importance. This has worsened not only the regional air quality but also has an indirect effect on the global air quality. The spatial and temporal variation of the source requires an accurate estimation of the surface emission which is the most essential parameter to study the air quality, that positively has been fulfilled in this study. The annual emission for 2020 is calculated to be 37.2 Gg/yr of PM 2.5 , 61.4 Gg/yr of PM 10 , 222.6 Gg/yr of CO, 131.3 Gg/yr of NO x , 60.3 Gg/yr of SO 2 , 120.4 Gg/yr of VOC, 9.5 Gg/yr of BC and 16.8 Gg/yr of OC that prevails in the toxic air of megacity Kolkata. The present surface chemistry dataset will be the first line of detailed information regarding emission hotspots in the megacity that could be used as important tool for clean air mitigation strategies, input into the air quality modeling study to tackle environmental issues, and public health. Article Highlights Identification of sources of pollutants through spatially resolved high-resolution inventory in megacity Kolkata. Unlike transport and industrial sector, municipal solid waste burning has emerged as another big source. Present information is vital for policy making to mitigate air quality issues and modeling studies.