India is vulnerable to all kinds of natural hazards associated with land, ocean, biosphere, atmosphere, and snow/glaciers. These natural hazards impact large areas and the population living in the affected regions. India is surrounded by ocean on three sides and is vulnerable to cyclonic activities. Every year cyclones hit the east and west coasts of India, affecting the population living along the coasts and infrastructure and inland areas. The extent of the affected inland areas depends on the intensity of the cyclone. On 12 October 2014, a strong cyclone “Hudhud” hit the east coast of India that caused a high degree of devastation along the coast. The impact of this cyclone was seen up to the Himalayan region. Detailed analysis of satellite and ground data show a strong coupling between land-ocean-atmosphere associated with the Hudhud cyclone. The contrast between land and ocean temperature was found to be closely related with the formation of the cyclone in the ocean and its movements towards land. Pronounced changes in the ocean, land, atmospheric, and meteorological parameters with the development of the cyclone and its landfall have been observed. Changes in total column ozone (TCO), relative humidity (RH), and volume mixing ratio of CO (CO VMR), water mixing ratio (H₂O MMR), surface latent heat flux (SLHF), and aerosol optical properties derived from satellite data show characteristic behavior of the Hudhud cyclone. Full article

The tropospheric NO₂ concentration from OMI AURA always shows high concentrations of NO₂ at a few locations in India, one of the high concentrations of NO₂ hotspots is associated with the locations of seven coal-fired Thermal Power plants (TPPs) in Singrauli. Emissions from TPPs are among the major sources of black carbon (BC) soot in the atmosphere. Knowledge of BC emissions from TPPs is important in characterizing regional carbonaceous particulate emissions, understanding the fog/haze/smog formation, evaluating regional climate forcing, modeling aerosol optical parameters and concentrations of black carbon, and evaluating human health. Furthermore, elevated BC concentrations, over the Indo-Gangetic Plain (IGP) and the Himalayan foothills, have emerged as an important subject to estimate the effects of deposition and atmospheric warming of BC on the accelerated melting of snow and glaciers in the Himalaya. For the first time, this study reports BC concentrations and aerosol optical parameters near dense coal-fired power plants and open cast coal mining adjacent to the east IGP. In-situ measurements were carried out in Singrauli (located in south-east IGP) at a fixed site about 10 km from power plants and in transit measurements in close proximity to the plants, for few days in the month of January and March 2013. At the fixed site, BC concentration up to the 95 μgm⁻³ is observed with strong diurnal variations. BC concentration shows two maxima peaks during early morning and evening hours. High BC concentrations are observed in close proximity to the coal-fired TPPs (>200 μgm⁻³), compared to the outside domain of our study region. Co-located ground-based sunphotometer measurements of aerosol optical depth (AOD) show strong spatial variability at the fixed site, with AOD in the range 0.38–0.58, and the highest AOD in the range 0.7–0.95 near the TPPs in transit measurements (similar to the peak of BC concentrations). Additionally, the Angstrom exponent was found to be in the range 0.4–1.0 (maximum in the morning time) and highest in the proximity of TPPs (~1.0), suggesting abundance of fine particulates, whereas there was low Angstrom exponent over the surrounding coal mining areas. Low Angstrom exponent is characterized by dust from the unpaved roads and nearby coal mining areas. MODIS derived daily AOD shows a good match with the MICROTOPS AOD. The CALIPSO derived subtypes of the aerosol plot shows that the aerosols over Singrauli region are mainly dust, polluted dust, and elevated smoke. The preliminary study for few days provides information about the BC concentrations and aerosol optical properties from Singrauli (one of the NO₂ hotspot locations in India). This preliminary study suggests that long-term continuous monitoring of BC is needed to understand the BC concentrations and aerosol optical properties for better quantification and the estimation of the emission to evaluate radiative forcing in the region. Full article

There has been growing interest in extending the coverage of ground particulate matter with aerodynamic diameter ≤ 2.5 μm (PM_2.5) monitoring networks based on satellite remote sensing data. With broad spatial and temporal coverage, a satellite-based monitoring network has a strong potential to complement the ground monitor system in terms of the spatiotemporal availability of the air quality data. However, most existing calibration models focus on a relatively small spatial domain and cannot be generalized to a national study. In this paper, we proposed a statistically reliable and interpretable national modeling framework based on Bayesian downscaling methods to be applied to the calibration of the daily ground PM_2.5 concentrations across the conterminous United States using satellite-retrieved aerosol optical depth (AOD) and other ancillary predictors in 2011. Our approach flexibly models the PM_2.5 versus AOD and the potential related geographical factors varying across the climate regions and yields spatial- and temporal-specific parameters to enhance model interpretability. Moreover, our model accurately predicted the national PM_2.5 with an R² at 70% and generated reliable annual and seasonal PM_2.5 concentration maps with its SD. Overall, this modeling framework can be applied to national-scale PM_2.5 exposure assessments and can also quantify the prediction errors. Full article

While the progress of China’s industrialization and urbanization has made great strides, atmospheric pollution has become the norm, with a wide range of influence and difficult governance. While many previous works on NOx pollution have been developed from the perspectives of natural science and technology, few studies have been conducted from social-economic points of view, and regional differences have not been given adequate attention in driving force models. This paper adopts China’s provincial panel data from 2006 to 2015, an extended STIRPAT (Stochastic Impacts by Regression on Population, Affluence and Technology) model, and spatial econometric models to investigate the socio-economic influential factors and spatial-temporal patterns of NOx emissions. According to the spatial correlation analysis results, the provincial NOx emission changes not only affected the provinces themselves, but also neighboring regions. Spatial econometric analysis shows that the spatial effect largely contributes to NOx emissions. The other explanatory variables all have positive impacts on NOx emissions, except for the vehicular indicator (which did not pass the significance test). As shown through the estimated consequences of direct and indirect effects, the indicators have significant positive effects on their own areas, and exacerbate NOx pollution. In terms of indirect effects, only three factors passed the significant test. An increase in gross domestic product (GDP) and energy consumption will exacerbate adjacent NOx pollution. Finally, a series of socio-economic measures and regional cooperation policies should be applied to improve the current air environment in China. Full article

Air pollution is defined as a phenomenon harmful to the ecological system and the normal conditions of human existence and development when some substances in the atmosphere exceed a certain concentration. In the face of increasingly serious environmental pollution problems, scholars have conducted a significant quantity of related research, and in those studies, the forecasting of air pollution has been of paramount importance. As a precaution, the air pollution forecast is the basis for taking effective pollution control measures, and accurate forecasting of air pollution has become an important task. Extensive research indicates that the methods of air pollution forecasting can be broadly divided into three classical categories: statistical forecasting methods, artificial intelligence methods, and numerical forecasting methods. More recently, some hybrid models have been proposed, which can improve the forecast accuracy. To provide a clear perspective on air pollution forecasting, this study reviews the theory and application of those forecasting models. In addition, based on a comparison of different forecasting methods, the advantages and disadvantages of some methods of forecasting are also provided. This study aims to provide an overview of air pollution forecasting methods for easy access and reference by researchers, which will be helpful in further studies. Full article