Aerosols are tiny particles (liquid or solid) suspended in the atmosphere. They play a significant rolein climate dynamics directly or indirectly. Aerosol Optical Depth (AOD) and Angstrom Exponent(AE) are significant parameters to study the concentration and size or type of aerosol over an area,respectively. In this article, we utilized three years of AOD and AE parameters derived from moderateresolution imaging spectroradiometer (MODIS) satellite during the period January, 2013 to December,2015 over Ethiopia. In order to study the spatiotemporal pattern of aerosols, we choose three areas(Debretabour, Gojjam and Addis Ababa) over Ethiopian highlands, which are representative of nonindustrial, agricultural and industrial areas respectively. Further we compare continental aerosols withmarine aerosols from Djibouti. Our results clearly depicts the aerosol distribution over Ethiopia ishighly variable spatially and temporally. The results indicates that the urban and biomass aerosols aredominate over Addis Ababa, and Gojjam respectively, whereas dust and biomass aerosols are presentover Debretabour, while Djibouti is loaded by sea spray aerosols. The seasonal variability of AOD isfound to be maximum during the kiremt (summer) and minimum during bega (winter) over all areas(continental and marine).

Based on the wind erosion equation and the use of moderate resolution imaging spectroradiometer (MODIS) satellite remote sensing data combined with parameter normalization processing, an optimized high spatial-temporal resolution soil fugitive dust (SFD) emission inventory compiling method was proposed in this study. The "2 + 26" cities in northern China, where heavy pollution frequently occurs, were used as a case study. Using the optimized method, we estimated that the PM₅₀, PM₁₀, and PM₂.₅ emissions from SFD of "2 + 26" cities in 2018 were 2,014,927, 1,007,463, and 151,120 tons, respectively. The dust emissions and emission factors of each city presented significant differences and were generally of a greater level in high-latitude areas (such as cities in Hebei Province) than in low-latitude areas (such as cities in Henan and Shandong Province). Moreover, with an increase in latitude, vegetation cover factors generally exhibit an upward trend, while temperature and rainfall exhibit a downward trend. The dust emissions in the different months showed significant differences. The total dust emission reached the highest level in "late winter–early spring" season (February to April), and the monthly emission accounted for 15–17% of the annual emissions. While in the "summer–autumn" season (July to November), it is the lowest level of the whole year, monthly emissions accounted for 3–5% of the annual emissions. The emission inventory method proposed in this study can provide a reference for dust emission assessment and further pollution prevention and control work.

In this study, we integrated a remote-sensing fire product (MOD14A1) and land-use product (MCD12Q1) to extract the number of crop-residue burning (CRB) spots and the fire radiative power (FRP) in China from 2001 to 2018. Moreover, we conducted three trend analyses and two geographic distribution analyses to quantify the interannual variations and summarize the spatial characteristics of CRB on grid (0.25° × 0.25°) and regional scales. The results indicated that CRB presents distinctive seasonal patterns with each sub-region. All trend analyses suggested that the annual number of CRB spots in China increased significantly from 2001 to 2018; the linear trend reached 2615 spots/year, the Theil-Sen slope was slightly lower at 2557 spots/year, and the Mann-Kendal τ was 0.75. By dividing the study period into two sub-periods, we found that the five sub-regions presented different trends in the first and second sub-periods; e.g., the Theil-Sen slope of eastern China in the first sub-period (2001–2009) was 1021 spots/year but was −1599 spots/year in the second period (2010–2018). This suggests that summer CRB has been effectively mitigated in eastern China since 2010. Further, the average FRP of CRB spots presented a decreasing trend from 27.5 MW/spot in 2001 to only 15.8 MW/spot in 2018; this may be attributable to more scattered CRB rather than aggregated CRB. Collectively, the fire spots, FRP, and average FRP indicated that spring, summer, and autumn CRB had dropped dramatically over previous levels by 2018 due to strict mitigation measures by local governments.

Satellite observations may improve the areal coverage of particulate matter (PM) air quality data that nowadays is based on surface measurements. Three statistical methods for retrieving daily PM2.5 concentrations from satellite products (MODIS-AOD, OMI-AAI) over the San Joaquin Valley (CA) are compared – Linear Regression (LR), Generalized Additive Models (GAM), and Multivariate Adaptive Regression Splines (MARS). Simple LRs show poor correlations in the western USA (R2 ≅ 0.2). Both GAM and MARS were found to perform better than the simple LRs, with a slight advantage to the MARS over the GAM (R2 = 0.71 and R2 = 0.61, respectively). Since MARS is also characterized by a better computational efficiency than GAM, it can be used for improving PM2.5 retrievals from satellite aerosol products. Reliable PM2.5 retrievals can fill in missing surface measurements in areas with sparse ground monitoring coverage and be used for evaluating air quality models and as exposure metrics in epidemiological studies.

The Moderate Resolution Imaging Spectroradiometer (MODIS) provides daily global coverage, but the 10 km resolution of its aerosol optical depth (AOD) product is not adequate for studying spatial variability of aerosols in urban areas. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for MODIS which provides AOD at 1 km resolution. Using MAIAC data, the relationship between MAIAC AOD and PM2.5 as measured by the EPA ground monitoring stations was investigated at varying spatial scales. Our analysis suggested that the correlation between PM2.5 and AOD decreased significantly as AOD resolution was degraded. This is so despite the intrinsic mismatch between PM2.5 ground level measurements and AOD vertically integrated measurements. Furthermore, the fine resolution results indicated spatial variability in particle concentration at a sub-10 km scale. Finally, this spatial variability of AOD within the urban domain was shown to depend on PM2.5 levels and wind speed.

Potential nuclear accidents propel serious environmental pollution, and the resultant radionuclide release devastates severely the environment severely and threatens aquatic organism survival. Likewise, ongoing climate change coupled with the gradual increase in global surface temperatures can also adversely impact the aquatic ecosystems. In the present study, we preconditioned zebrafish (Danio rerio) at three different temperatures (18 °C, 26 °C and 34 °C) to investigate the effects of a temperature profile on their radiosensitivity (exposure to 20 Gy of gamma rays) to identify the potential biochemical mechanism responsible for influencing radiosensitivity. We found that preconditioning of zebrafish at different temperatures moulded specific gut microbiota configurations and impacted hepatic glycometabolism and sensitivity to subsequent radiation. Following antibiotic treatment to reduce gut bacteria, these observed differences in the expression of hepatic glycometabolism-related genes and radiation-induced intestinal toxicity were minimal, supporting the hypothesis that the gut bacteria reshaped by different ambient temperatures might be the key modulators of hepatic functions and radiosensitivity in zebrafish. Together, our findings provide novel insights into the connection of radiation injuries with temperature alterations in fish, and suggest that maintaining the stability of gram-positive bacteria may be efficacious to protect aquatic organisms against short or long-term radioactive contamination in the context of global climate change.

Crop residue open burning has substantial negative effects on air quality, human health, and climate change, and accurate and timely estimates of its air pollutant emissions are essential. Open burning proportion (OBP) is the key parameter in estimating the emission from the crop residue open burning by bottom-up method. However, the OBP is mainly obtained by field investigation, which consumes much time, manpower and financial resources, leading to the OBP data deficient seriously. In this study, the significant logarithmic relations were found between OBP and fire radiative energy (FRE), and then the FRE-based OBP estimation models were developed for different regions of China. The comparison between the FRE-based OBP and the field-investigated OBP illustrated the reliability of the developed models (r = 0.71, NMB = −8% and NME = 25%). The OBPs of different municipalities/provinces in mainland China from 2003 to 2018 were further calculated. The results showed that the estimated OBP variation exhibited fluctuating upward trend with annual mean growth rate of 3.7% from 2003 to 2014, while dramatically decreased with annual mean reduction rate of 5.9% from 2014 to 2018. The estimation accuracy of emission from open biomass burning can also be can be significantly improved by basing on the year-specific OBP, compared with the calculation based on fixed OBP. The annual PM₂.₅ emissions would decrease 4.5%–25.9% and increase 6.6%–30.7% in the scenarios of a fixed OBP during 2003–2014 and 2014–2018, respectively. The developed models complemented the severely missing OBP data of mainland China for the first time. By combining the advantages of bottom-up approach and FRE method, the proposed FRE-based models can avoid their disadvantages, and can help to get more accurately and timely emissions from crop residue open burning.

Dissolved oxygen (DO) is an effective indicator for water pollution. However, since DO is a non-optically active parameter and has little impact on the spectrum captured by satellite sensors, research on estimating DO by remote sensing at multiple spatiotemporal scales is limited. In this study, the support vector regression (SVR) models were developed and validated using the remote sensing reflectance derived from both Landsat and Moderate Resolution Imaging Spectroradiometer (MODIS) data and synchronous DO measurements (N = 188) and water temperature of Lake Huron and three other inland waterbodies (N = 282) covering latitude between 22–45 °N. Using the developed models, spatial distributions of the annual and monthly DO variability since 1984 and the annual monthly DO variability since 2000 in Lake Huron were reconstructed for the first time. The impacts of five climate factors on long-term DO trends were analyzed. Results showed that the developed SVR-based models had good robustness and generalization (average R² = 0.91, root mean square percentage error = 2.65%, mean absolute percentage error = 4.21%), and performed better than random forest and multiple linear regression. The monthly DO estimates by Landsat and MODIS data were highly consistent (average R² = 0.88). From 1984 to 2019, the oxygen loss in Lake Huron was 6.56%. Air temperature, incident shortwave radiation flux density, and precipitation were the main climate factors affecting annual DO of Lake Huron. This study demonstrated that using SVR-based models, Landsat and MODIS data could be used for long-term DO retrieval at multiple spatial and temporal scales. As data-driven models, combining spectrum and water temperature as well as extending the training set to cover more DO conditions could effectively improve model robustness and generalization.

In this analysis, the Aqua/MODIS aerosol optical thickness (AOD), Aura/OMI tropospheric NO2 and SO2 column concentration from 2006 to 2015 were used to statistically analyze the spatial distribution characteristics and variation trends of three polluted parameters from three temporal scales of monthly, seasonal and annual average. The results showed that the minimum values of NO2 and SO2 column concentrations both appeared in July and August, and the maximum values appeared in December and January, which was contrary to the variations in AOD. The highly polluted levels were mainly distributed in Shijiazhuang, Xingtai, and Yancheng cities of Hebei Province, and gradually transported to Zhengzhou, Henan Province, north and southwest of Shandong Province, and Tianjin, along the main line of Taiyuan-Linyi, Shanxi Province. AOD and NO2 had significant differences on the seasonal average scale, whereas SO2 had little changes. These pollutants had declined year by year since 2011, in the 10-year period, AOD and SO2 respectively decreased by 17.14% and 10.57%, and only NO2 rose from 8.69 × 1015 molecules/cm2 in 2006 to 9.10 × 1015 molecules/cm2 in 2015 with the increase rate of 4.79%. Integrated with MODIS-released fire products and the Multi-resolution Emission Inventory for China (MEIC), high AOD values in summer were usually accompanied by frequent biomass burning, and heavy heating demand of coal burning led to largest NO2 and SO2 levels in winter. Both inter-annual variations of MEIC NOx and OMI-observed NO2 responded to emission reductions of vehicle exhaustions positively, but vehicle population in Henan and Shandong provinces need to be further controlled. The significant decline of SO2 is mainly attributed to the enforcement of de-sulfurization devices in power plants. Our study found that in the treatment of complex atmospheric pollution, in addition to strict control of common sources of emissions from AOD, NO2 and SO2, it is also necessary to consider their individual characteristics.

Ground fine particulate matter (PM2.5) concentrations at high spatial resolution are substantially required for determining the population exposure to PM2.5 over densely populated urban areas. However, most studies for China have generated PM2.5 estimations at a coarse resolution (≥10 km) due to the limitation of satellite aerosol optical depth (AOD) product in spatial resolution. In this study, the 3 km AOD data fused using the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 AOD products were employed to estimate the ground PM2.5 concentrations over the Beijing-Tianjin-Hebei (BTH) region of China from January 2013 to December 2015. An improved geographically and temporally weighted regression (iGTWR) model incorporating seasonal characteristics within the data was developed, which achieved comparable performance to the standard GTWR model for the days with paired PM2.5- AOD samples (Cross-validation (CV) R2 = 0.82) and showed better predictive power for the days without PM2.5- AOD pairs (the R2 increased from 0.24 to 0.46 in CV). Both iGTWR and GTWR (CV R2 = 0.84) significantly outperformed the daily geographically weighted regression model (CV R2 = 0.66). Also, the fused 3 km AODs improved data availability and presented more spatial gradients, thereby enhancing model performance compared with the MODIS original 3/10 km AOD product. As a result, ground PM2.5 concentrations at higher resolution were well represented, allowing, e.g., short-term pollution events and long-term PM2.5 trend to be identified, which, in turn, indicated that concerns about air pollution in the BTH region are justified despite its decreasing trend from 2013 to 2015.