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).

The space-borne measured fine-mode aerosol optical depth (fAOD) is a gross index of column-integrated anthropogenic particulate pollutants, especially over the populated land. The fAOD is the product of the AOD and the fine-mode fraction (FMF). While there exist numerous global AOD products derived from many different satellite sensors, there have been much fewer, if any, global FMF products with a quality good enough to understand their spatiotemporal variations. This is key to understanding the global distribution and spatiotemporal variations of air pollutants, as well as their impacts on global environmental and climate changes. Modifying our newly developed retrieval algorithm to the latest global-scale Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol product (Collection 6.1), a global 10-year FMF product is generated and analyzed here. We first validate the product through comparisons with the FMF derived from Aerosol Robotic Network (AERONET) measurements. Among our 169,313 samples, the satellite-derived FMFs agreed with the AERONET spectral deconvolution algorithm (SDA)-retrieved FMFs with a root-mean-square error (RMSE) of 0.22. Analyzed using this new product are the global patterns and interannual and seasonal variations of the FMF over land. In general, the FMF is large (>0.80) over Mexico, Myanmar, Laos, southern China, and Africa and less than 0.5 in the Sahelian and Sudanian zones of northern Africa. Seasonally, higher FMF values occur in summer and autumn. The linear trend in the satellite-derived and AERONET FMFs for different countries was explored. The upward trend in the FMFs was particularly strong over Australia since 2008. This study provides a new global view of changes in FMFs using a new satellite product that could help improve our understanding of air pollution around the world.

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.

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.

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.

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.

In recent decades China has experienced high-level PM₂.₅ pollution and then visible air quality improvement. To understand the air quality change from the perspective of aerosol optical depth (AOD), we adopted two statistical methods of Empirical Orthogonal Functions (EOF) and Non-negative Matrix Factorization (NMF) to AOD retrieved by MODIS over China and surrounding areas. Results showed that EOF and NMF identified the important factors influencing AOD over China from different angles: natural dusts controlled the seasonal variation with contribution of 42.4%, and anthropogenic emissions have larger contribution to AOD magnitude. To better observe the interannual variation of different sources, we removed seasonal cycles from original data and conducted EOF analysis on AOD monthly anomalies. Results showed that aerosols from anthropogenic sources had the greatest contribution (27%) to AOD anomaly variation and took an obvious downward trend, and natural dust was the second largest contributor with contribution of 17%. In the areas surrounding China, the eastward aerosol transport due to prevailing westerlies in spring significantly influenced the AOD variation over West Pacific with the largest contribution of 21%, whereas the aerosol transport from BTH region in winter had relative greater impact on the AOD magnitude. After removing seasonal cycles, biomass burning in South Asia became the most important influencing factor on AOD anomalies with contribution of 10%, as its interannual variability was largely affected by El Niño. Aerosol transport from BTH was the second largest contributor with contribution of 8% and showed a decreasing trend. This study showed that the downward trend of AOD over China since 2011 was dominated by aerosols from anthropogenic sources, which in a way confirmed the effectiveness of air pollution control policies.

In this study, various remote sensing data, modeling data and emission inventories were integrated to analyze the tempo-spatial distribution of biomass burning in mainland Southeast Asia and its effects on the local ambient air quality from 2001 to 2016. Land cover changes have been considered in dividing the biomass burning into four types: forest fires, shrubland fires, crop residue burning and other fires. The results show that the monthly average number of fire spots peaked at 34,512 in March and that the monthly variation followed a seasonal pattern, which was closely related to precipitation and farming activities. The four types of biomass burning fires presented different tempo-spatial distributions. Moreover, the monthly Aerosol Optical Depth (AOD), concentration of particulate matter with a diameter less than 2.5 μm (PM₂.₅) and carbon monoxide (CO) total column also peaked in March with values of 0.62, 45 μg/m³ and 3.25 × 10¹⁸ molecules/cm², respectively. There are significant correlations between the monthly means of AOD (r = 0.74, P < 0.001), PM₂.₅ concentration (r = 0.88, P < 0.001), and CO total column (r = 0.82, P < 0.001) and the number of fire spots in the fire season. We used Positive Matrix Factorization (PMF) model to resolve the sources of PM₂.₅ into 3 factors. The result indicated that the largest contribution (48%) to annual average concentration of PM₂.₅ was from Factor 1 (dominated by biomass burning), followed by 27% from Factor 3 (dominated by anthropogenic emission), and 25% from Factor 2 (long-range transport/local nature source). The annually anthropogenic emission of CO and PM₂.₅ from 2001 to 2012 and the monthly emission from the Emission Database for Global Atmosphere Research (EDGAR) were consistent with PMF analysis and further prove that biomass burning is the dominant cause of the variation in the local air quality in mainland Southeast Asia.

China has performed crop residue burning (CRB) for a long time and has suffered from resultant environmental pollution. High temporal resolution has not been fully discussed in attempts to address the temporal and spatial impact of CRB in China on air quality. Our study used the MOD14A1 product of the MODerate resolution Imaging Spectrometer (MODIS) to extract the daily CRB for China during the period from 2014 to 2016, and the daily aerosol optical depth (AOD) provided by MODIS Collection 6 was obtained to simultaneously reflect the air pollution. First, the study area was classified into five subregions. A temporal analysis was conducted on the daily variation in the number of CRB events and the regional mean value of AOD, the spatial contribution ratio of CRB on aerosol pollution was then calculated, and finally, a temporal and spatial Pearson correlation was calculated to find the spatially varying relationship between CRB and aerosol. The results suggest the following: (1) CRB possesses seasonal characteristics that are associated with the harvest time or sowing time of major crops in the region. (2) The impact of CRB on aerosol was delayed by 1–6 days. (3) High contribution ratios (70%–90%) occurred in northeast China on a large scale; even when the impact of the CRB on aerosol pollution in the Huang-Huai-Hai river basin occurred on a large scale, the value was merely approximately 30%. Relatively low contributions of CRB have been found in other places, whereas the contribution of CRB was severe in some places with high-density populations. (4) Temporal-spatial correlation provided an accurate index to reflect the correlation of CRB and aerosol in a specific location, which suggests that, in places with large scale and dense CRB, CRB tends to have a high positive correlation with aerosol pollution for each day.