The Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) is widely used as an advanced model dataset for the understanding of global climate change. However, independent validation and comparison of MERRA-2 are both insufficient and always desired. Therefore, in this study, the quantitative evaluation of MERRA-2 aerosol products was conducted over China for Aerosol Optical Depth (AOD), Angstrom exponent (AE), absorbing AOD (AAOD) and chemical components (black carbon (BC) and dust (DU)) using Sun sky radiometer Observation NETwork (SONET) ground-based measurements and POLDER-3/PARASOL satellite products generated by the GRASP algorithm. The available MERRA-2 monthly dataset and PARASOL/GRASP monthly and seasonal products were intercompared over China. MERRA-2 AOD (550 nm) show general good agreement with SONET and PARASOL/GRASP. For example, the correlation coefficients are usually 0.6–0.85 with SONET and 0.75–0.85 with PARASOL/GRASP, the bias is usually −0.293 to +0.008 with SONET. For AE and AAOD, the agreement is still reasonable. MERRA-2 is found to overestimate fine mode AE and to display a general underestimation of aerosol absorption over China. In addition, MERRA-2 BC and DU mass concentrations show spatial and quantitative consistency with PARASOL/GRASP components climatological products. The relatively high columnar BC mass concentration is observed around 1.5–2 mg/m3 over the East China industrial region and high DU mass concentration is around 150 mg/m3 near Taklimakan desert. MERRA-2 shows slightly higher BC and lower DU concentration than PARASOL/GRASP over East China. The evaluations with in situ BC measurements near surface verify the overestimation (MAE = +0.44 µg/m2) of MERRA-2 and underestimation (MAE = −0.38 µg/m2) of PARASOL/GRASP. The analysis demonstrates multi-source datasets, such as ground-based, space-borne remote sensing, in situ measurements, model simulation as well as reanalysis data, complement each other and can be used to refine aerosol characterization.

/pdf/evaluation-of-merra-2-aerosol-optical-and-component-eyweqj95.pdf

Evaluation of MERRA-2 Aerosol Optical and Component Properties over China Using SONET and PARASOL/GRASP Data

Coupling interactions and spatial equilibrium analysis of water-energy-food in the Yellow River Basin, China

Pollution in the world and exposure of humans and nature to toxic substances is continuously worsening at a rapid pace. In the last 60 years, human and domestic animal health has been challenged by continuous exposure to toxic substances and pollutants because of uncontrolled growth, modernization, and industrialization. More than 350,000 new chemicals have been introduced to our lives, mostly without any reasonable control of their health effects and toxicity. A plethora of studies show exposure to these harmful substances during this period with their implications on the skin and mucosal epithelial barrier and increasing prevalence of allergic and autoimmune diseases in the context of the "epithelial barrier hypothesis". Exposure to these substances causes an epithelial injury with peri-epithelial inflammation, microbial dysbiosis and bacterial translocation to sub-epithelial areas, and immune response to dysbiotic bacteria. Here, we provide scientific evidence on the altered human exposome and its impact on epithelial barriers.

Effect of altered human exposome on the skin and mucosal epithelial barrier integrity.

High concentrations of PM2.5 and ozone (O3) seriously threaten human health. In this study, we constructed a machine learning-based model to predict PM2.5 and O3 concentrations, with the Fenwei Plain in China, as our study target. We evaluated the performance of RF, XGB, and CatBoost models for predicting PM2.5 and O3 concentrations and found that the CatBoost model performed the best, capturing most of the PM2.5 and O3 concentration changes (with R2 values above 0.7). Using the SHAP-based machine learning interpretation method, we analyzed the factors contributing to the variation of PM2.5 and O3 concentrations. We found that PM10 had the largest effect on PM2.5 concentration, while net surface solar radiation significantly affected O3 concentration. Additionally, meteorological variables such as temperature and humidity play a role in the variation of PM2.5 and O3 concentrations. Finally, based on the results of the SHAP analysis, we propose some feasible management methods for PM2.5 and O3, providing suggestions for managing PM2.5 and O3 concentrations. 

PM2.5 and O3 concentration estimation based on interpretable machine learning

The epithelial barrier theory links the recent rise in chronic non-communicable diseases, notably autoimmune and allergic disorders, to environmental agents disrupting the epithelial barrier. Global pollution and environmental toxic agent exposure have worsened over six decades because of uncontrolled growth, modernisation, and industrialisation, affecting human health. Introducing new chemicals without any reasonable control of their health effects through these years has led to documented adverse effects, especially on the skin and mucosal epithelial barriers. These substances, such as particulate matter, detergents, surfactants, food emulsifiers, micro- and nano-plastics, diesel exhaust, cigarette smoke and ozone, have been shown to compromise the epithelial barrier integrity. This disruption is linked to the opening of the tight junction barriers, inflammation, cell death, oxidative stress and metabolic regulation. Consideration must be given to the interplay of toxic substances, underlying inflammatory diseases, and medications, especially in affected tissues. This review article discusses the detrimental effect of environmental barrier-damaging compounds on human health and involves cellular and molecular mechanisms.

Recent advances in the epithelial barrier theory.

A long-term analysis of atmospheric black carbon MERRA-2 concentration over China during 1980–2019

Air pollution has seriously endangered human health and the natural ecosystem during the last decades. Air quality monitoring stations (AQMS) have played a critical role in providing valuable data sets for recording regional air pollutants. The spatial representativeness of AQMS is a critical parameter when choosing the location of stations and assessing effects on the population to long-term exposure to air pollution. In this paper, we proposed a methodological framework for assessing the spatial representativeness of the regional air quality monitoring network and applied it to ground-based PM2.5 observation in the mainland of China. Weighted multidimensional Euclidean distance between each pixel and the stations was used to determine the representativeness of the existing monitoring network. In addition, the K-means clustering method was adopted to improve the spatial representativeness of the existing AQMS. The results showed that there were obvious differences among the representative area of 1820 stations in the mainland of China. The monitoring stations could well represent the PM2.5 spatial distribution of the entire region, and the effectively represented area (i.e. the area where the Euclidean distance between the pixels and the stations was lower than the average value) accounted for 67.32% of the total area and covered 93.12% of the population. Forty additional stations were identified in the Northwest, North China, and Northeast regions, which could improve the spatial representativeness by 14.31%. 

Understanding the spatial representativeness of air quality monitoring network and its application to PM2.5 in the mainland china

Black carbon (BC) is an important component of airborne fine particulate matter, with significant impacts on global climate change and human health. Taking Minhang District of Shanghai as the study area, a microaethalometer (MA200) and GPS were installed on the electric taxi to form a mobile observation platform to identify the spatial distribution and hot spots of atmospheric BC in urban environment. We analyzed the sources and influencing factors of BC. The results showed that the overall characteristics of the spatial distribution pattern of near surface atmospheric BC in Minhang District of Shanghai were high in the north and low in the south. The average BC concentration was (4.11±4.87) μg·m-3. The average concentrations of BC in working days and non-working days were (4.22±1.49) and (3.52±2.26) μg·m-3. The variability of BC concentration in the high value area was large, indicating that the increases of BC concentration in mobile observation were related to traffic accidents in the road section. In addition to human activities, large-scale dense vegetation might inhibit BC diffusion. The Absorption ngström Exponent (AAE) was (0.82±0.54), which was closer to that of fossil fuel combustion. The contributions of fossil fuel emissions, biomass combustion, and mixed sources to BC sources were 67.5%, 4.9% and 27.6%, respectively.

[Spatial characteristics and source apportionment of urban environmental black carbon concentrations based on mobile monitoring].

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A long-term analysis of atmospheric black carbon MERRA-2 concentration over China during 1980–2019

Understanding the spatial representativeness of air quality monitoring network and its application to PM2.5 in the mainland china

[Spatial characteristics and source apportionment of urban environmental black carbon concentrations based on mobile monitoring].