Satellite observations for monitoring atmospheric NO2 in correlation with the existing pollution sources under arid environment

Updating and evaluating the NH3 gas-phase chemical mechanism of MOZART-4 in the WRF-Chem model

The accuracy of determining atmospheric chemical mechanisms is a key factor in air pollution prediction, pollution-cause analysis and the development of control schemes based on air quality model simulations. However, the reaction of NH3 and OH to generate NH2 and its subsequent reactions are often ignored in the MOZART-4 chemical mechanism. To solve this problem, the gas-phase chemical mechanism of NH3 was updated in this study. Response surface methodology (RSM), integrated gas-phase reaction rate (IRR) diagnosis and process analysis (PA) were used to quantify the influence of the updated NH3 chemical mechanism on the O3 simulated concentration, the nonlinear response relationship of O3 and its precursors, the chemical reaction rate of O3 generation and the meteorological transport process. The results show that the updated NH3 chemical mechanism can reduce the error between the simulated and observed O3 concentrations and better simulate the O3 concentration. Compared with the Base scenario (original chemical mechanism simulated), the first-order term of NH3 in the Updated scenario (updated NH3 chemical mechanism simulated) in RSM passed the significance test (p < 0.05), indicating that NH3 emissions have an influence on the O3 simulation, and the effects of the updated NH3 chemical mechanism on NOx-VOC-O3 in different cities are different. In addition, the analysis of chemical reaction rate changes showed that NH3 can affect the generation of O3 by affecting the NOx concentration and NOx circulation with radicals of OH and HO2 in the Updated scenario, and the change of pollutant concentration in the atmosphere leads to the change of meteorological transmission, eventually leading to the reduction of O3 concentration in Beijing. In conclusion, this study highlights the importance of atmospheric chemistry for air quality models to model atmospheric pollutants and should attract more research focus.

Impacts of cement industry air pollutants on the environment and satellite data applications for air quality monitoring and management

The cement industry plays critical role in any country's development and economic growth. Cement is extensively used in construction sector and infrastructural projects. Abundant raw material availability, infrastructure demands, urbanization, and recent government initiatives-Atal Mission for Rejuvenation and Urban Transformation (AMRUT) project and housing for all under the Pradhan Mantri Awas Yojana (PMAY), India-stood at second place in cement-producing country in the world. Cement plants are emitting 15% of global pollutions into the environment among various industries. Cement industry byproducts include dust/particulate matter (PM_2.5 and PM₁₀), toxic gases (COx, NO_x, SOx, CH₄, and VOCs), noise, and heavy metals (Cr, Ni, Co, Pb, and Hg), which cause climate change, global warming, and health risks as well as negative impact on flora and fauna. The Terra, Aura, Sentinel-5P, GOSAT, and other satellite datasets allow estimation of cement industry major air pollutants such as particulate matter (PM), sulfur dioxide (SO₂), nitrogen dioxide (NO₂), carbon dioxide (CO₂), and volatile organic compounds (VOCs) using regression models, artificial neural network-based models, machine learning models, and the tropospheric NO2 vertical column density (VCD) retrieval algorithm. This review article explores the evolution of the Indian cement industry, air pollutants from the cement industry, social and environmental implications, satellite datasets, models used for assessing air pollutants, and challenges for the long-term sustainability of the cement industry.

Effect of different plant communities on NO2 in an urban road greenbelt in Nanjing, China

As an important part of urban ecosystems, plants can reduce NO₂ concentrations in the air. However, there is little evidence of the effects of different plant communities on NO₂ concentrations in street-scale green spaces. We used a multifunctional lifting environmental detector to investigate the impact of environmental factors and small plant communities on NO₂ concentrations in street green spaces during the summer and winter in Nanjing, China. The results showed that temperature, atmospheric pressure, and noise were significantly (P < 0.05) correlated with seasonal changes, temperature and humidity significantly (P < 0.01) influenced NO₂ concentrations in winter and summer, and the average NO₂ concentration in summer was generally higher than in winter. By comparing NO₂ concentrations in different plant community structures and their internal spaces, we found that the plant community structure with tree-shrub-grass was more effective in reducing pollution. These findings will help predict the impact of plant communities on NO₂ concentrations in urban streets and help city managers and planners effectively reduce NO₂ pollution.

A Multidisciplinary Approach for Groundwater Potential Mapping in a Fractured Semi-Arid Terrain (Kerdous Inlier, Western Anti-Atlas, Morocco)

This study is focused on developing an approach for spatial mapping of groundwater by considering four types of factors (geological, topographical, hydrological, and climatic factors), and by using different bivariate statistical models, such as frequency ratio (FR) and Shannon’s entropy (SE). The developed approach was applied in a fractured aquifer basin (Ameln Basin, Western Anti-Atlas, Morocco), to map the spatial variation of groundwater potential. Fifteen factors (15) influencing groundwater were considered in this study, including slope degree, slope aspect, elevation, topographic wetness index (TWI), slope length (LS), topographic position index (TPI), plane curvature, profile curvature, drainage density, lineament density, distance to rivers and fault network, normalized difference vegetation index (NDVI), lithology, and land surface temperature (LST). The potential maps produced were then classified into five classes to illustrate the spatial view of each potential class obtained. The predictive capacity of the frequency ratio and Shannon’s entropy models was determined using two different methods, the first one based on the use of flow data from 49 boreholes drilled in the study area, to test and statistically calibrate the predictive capacity of each model. The results show that the percentage of positive water points corresponds to the most productive areas (high water flow) (42.86% and 30.61% for the FR and SE models, respectively). On the other hand, the low water flows are consistent with the predicted unfavorable areas for hydrogeological prospecting (4.08% for the FR model and 6.12% for the SE model). Additionally, the second validation method involves the integration of 7200 Hz apparent resistivity data to identify conductive zones that are groundwater circulation zones. The interpretation of the geophysical results shows that the high-potential zones match with low apparent resistivity zones, and therefore promising targets for hydrogeological investigation. The FR and SE models have proved very efficient for hydrogeological mapping at a fractured basement area and suggest that the northern and southern part of the study area, specifically the two major fault zones (Ameln Valley in the north, and the Tighmi-Tifermit Valley in the south) has an adequate availability of groundwater, whereas the central part, covering the localities of Tarçouat, Boutabi, Tililan, and Ighalen, presents a scarcity of groundwater. The trend histogram of the evolution of positive water points according to each potentiality class obtained suggests that the FR model was more accurate than the SE model in predicting the potential groundwater areas. The results suggest that the proposed approach is very important for hydrogeological mapping of fractured aquifers, and the resulting maps can be helpful to managers and planners to generate groundwater development plans and attenuate the consequences of future drought.