It has been more than 20 years that the Measurement of Pollution in The Troposphere (MOPITT) mission onboard the NASA Terra satellite keeps providing us CO atmospheric concentration measurements around the globe. The current paper observes CO mixing ratio from the MOPITT Version 8 (MOP03J_V008) instrument in order to study the spatiotemporal analysis of CO (spanning from April 2000 to February 2020) in the Troposphere of Iran. Results indicate that the average CO in Iran’s troposphere has been 133.5 ppbv (i.e., 5.5 ppbv lower than the global mean CO). The highest distribution of CO (with an average of 150 ppbv) belongs to the city of Tehran (the capital of Iran) as well as the Caspian Sea coastal area, while the lowest value (with an average of less than 110 ppbv) has been estimated on the Zagros Mountains (southwestern Iran). The highest and lowest CO values have been observed in cold and hot months, respectively. Seasonally speaking, it is also clear that the highest and lowest carbon monoxide values occur in winter and summer, respectively. The vertical profile of MOPITT CO shows the maximum CO concentration at lower levels of the troposphere. It has been expanded up to 150 hPa. The trend is investigated by means of Pearson correlation coefficient statistical method. Overall, long-term monitoring of MOPITT CO in Iran indicates a decreasing trend of tropospheric CO over the 20 years (Y=-0.008X+449.31). Possible reasons for such a decrease can be related to improved transportation fleet, increased fuel quality, plans for traffic control, promotion of heating systems, and promotion of industrial fuels and factories.

Forest health condition was evaluated on 111 terrestrial permanent monitoring plots. Image classification for the whole region was done by using regression equation between data from the terrestrial survey and digital value of original and derived synthetic bands of Landsat TM. It was found that synthetic channels give better result than original bands. The change analysis was carried out by the method of image diffferences in image pairs. Output images were standardized and than reclassified into 6 classes. It was found that difference of vegetation indexes between two years gives better result than simple difference between two independent classified images of forest condition

Spatiotemporal resolved models were developed predicting daily fine particulate matter (PM₂.₅) concentrations across Switzerland from 2003 to 2013. Relatively sparse PM₂.₅ monitoring data was supplemented by imputing PM₂.₅ concentrations at PM₁₀ sites, using PM₂.₅/PM₁₀ ratios at co-located sites. Daily PM₂.₅ concentrations were first estimated at a 1 × 1km resolution across Switzerland, using Multiangle Implementation of Atmospheric Correction (MAIAC) spectral aerosol optical depth (AOD) data in combination with spatiotemporal predictor data in a four stage approach. Mixed effect models (1) were used to predict PM₂.₅ in cells with AOD but without PM₂.₅ measurements (2). A generalized additive mixed model with spatial smoothing was applied to generate grid cell predictions for those grid cells where AOD was missing (3). Finally, local PM₂.₅ predictions were estimated at each monitoring site by regressing the residuals from the 1 × 1km estimate against local spatial and temporal variables using machine learning techniques (4) and adding them to the stage 3 global estimates. The global (1 km) and local (100 m) models explained on average 73% of the total,71% of the spatial and 75% of the temporal variation (all cross validated) globally and on average 89% (total) 95% (spatial) and 88% (temporal) of the variation locally in measured PM₂.₅ concentrations.

Rapid economic growth, industrialization, and urbanization have caused frequent air pollution events in East Asia over the last few decades. Recently, aerosol data from geostationary satellite sensors have been used to monitor ground-level particulate matter (PM) concentrations hourly. However, many studies have focused on using historical datasets to develop PM estimation models, often decreasing their predictability for unseen data in new days. To mitigate this problem, this study proposes a novel real-time learning (RTL) approach to estimate PM with aerodynamic diameters of <10 μm (PM₁₀) and <2.5 μm (PM₂.₅) using hourly aerosol data from the Geostationary Ocean Color Imager (GOCI) and numerical model outputs for daytime conditions over Northeast Asia. Three schemes with different weighting strategies were evaluated using 10-fold cross-validation (CV). The RTL models, which considered both concentration and time as weighting factors (i.e., Scheme 3) yielded consistent improvement for 10-fold CV performance on both hourly and monthly scales. The real-time calibration results for PM₁₀ and PM₂.₅ were R² = 0.97 and 0.96, and relative root mean square error (rRMSE) = 12.1% and 12.0%, respectively, and the 10-fold CV results for PM₁₀ and PM₂.₅ were R² = 0.73 and 0.69 and rRMSE = 41.8% and 39.6%, respectively. These results were superior to results from the offline models in previous studies, which were based on historical data on an hourly scale. Moreover, we estimated PM concentrations in the ocean without using land-based variables, and clearly demonstrated the PM transport over time. Because the proposed models are based on the RTL approach, the density of in-situ monitoring sites could be a major uncertainty factor. This study identified that a high error occurred in low-density areas, whereas a low error occurred in high-density areas. The proposed approach can be operated to monitor ground-level PM concentrations in real-time with uncertainty analysis to ensure optimal results.

In East Asia, air quality has been recognized as an important public health problem. In particular, the surface concentrations of air pollutants are closely related to human life. This study aims to develop models for estimating high spatial resolution surface concentrations of NO₂ and O₃ from TROPOspheric Monitoring Instrument (TROPOMI) data in East Asia. The machine learning was adopted by fusion of various satellite-based variables, numerical model-based meteorological variables, and land-use variables. Four machine learning approaches—Support Vector Regression (SVR), Random Forest (RF), Extreme Gradient Boost (XGB), and Light Gradient Boosting Machine (LGBM)—were evaluated and compared with Multiple Linear Regression (MLR) as a base statistical method. This study also modeled the NO₂ and O₃ concentrations over the ocean surface (i.e., land model for scheme 1 and ocean model for scheme 2). The estimated surface concentrations were validated through three cross-validation approaches (i.e., random, temporal, and spatial). The results showed that the NO₂ model produced R² of 0.63–0.70 and normalized root-mean-square-error (nRMSE) of 38.3–42.2% and the O₃ model resulted in R² of 0.65–0.78 and nRMSE of 19.6–24.7% for scheme 1. The indirect validation based on the stations near the coastline for scheme 2 showed slight decrease (~0.3–2.4%) in nRMSE when compared to scheme 1. The contributions of input variables to the models were analyzed based on SHapely Additive exPlanations (SHAP) values. The NO₂ vertical column density among the TROPOMI-derived variables showed the largest contribution in both the NO₂ and O₃ models.

It has been established from previous studies that chlorophyll-a surface concentration has been declining in the eastern English Channel. This decline has been attributed to a decrease in nutrient concentrations in the rivers. However, the decrease in river discharge could also be a cause. In our study, rivers outflows and in-situ data have been compared to time series of satellite-derived chlorophyll-a concentrations. Dynamic Linear Model has been used to extract the dynamic and seasonally adjusted trends of several environmental variables. The results showed that, for the 1998–2019 period, chlorophyll-a levels stayed significantly lower than average and satellite images revealed a coast to offshore gradient. Chlorophyll-a concentration of coastal stations appeared to be related to the declining fluxes of phosphate while offshore stations were more related to nitrate-nitrite. Therefore, we can exclude that the climate variability, through river flows alone, has a dominant effect on the decline of chlorophyll-a concentration.

A comprehensive research was conducted to analyze the formation and characteristics of continuous air pollution during winter in Wuhan, China, based on ground and satellite joint observation. The effect of meteorological conditions, the source of pollutants and the optical properties of aerosols were investigated. The pressure and the accumulation of pollutants were the two main causes of continuous haze formation. The continuous cold high-pressure system, accompanied by a stable inversion layer, limited the contaminants below the height of 700 m on 15–23 January. The height of the boundary layer was below 1 000 m, based on the lidar observation. Meteorological condition contributes to the accumulation of pollutants. Then, dust transport and local anthropogenic pollutant emissions promoted the accumulation of pollutants, resulting in continuous haze pollution. Different from the heavy pollution (the 24 h-average PM2.5 is more than 200.0 μgm−3) over the Beijing-Tianjin-Hebei region, the contaminants in the Wuhan area were mainly primary pollutants, including airborne dust and anthropogenic pollutants. Moreover, a photochemical reaction was observed. However, the extent of secondary pollution formation was not high during haze pollution. Result in the particle size distribution confirmed the process of dust transport. Fine-mode and coarse-mode particles sometimes appear in the haze pollution in winter. According to the satellite data, the AOD maintained a large level of approximately 0.8 during the pollution. The aerosol extinction ability was relatively strong during the pollution period, whether aerosol is absorbed or a scattering effect dominated. In this study, the formation process of haze pollution revealed which can be used to validate air-quality models over the Wuhan region and can also provide guidance for government for the prevention work of haze pollution over Central China.

This study aimed to examine the main characteristics of tropospheric nitrogen dioxide (NO2) concentrations over the Northeast Asia, using the Ozone Monitoring Instrument (OMI) data from 2005 to 2010. The annual mean NO2 concentrations (AMNC) had an increasing trend mainly due to increasing NO2 emissions in China except during the 2008 Beijing Olympic Games period, while the reduction policies of South Korea and Japan have led it to be stagnant or decreased. To investigate further regional characteristics of NO2 increasing trends in China, we divided our study area into 6 geographical regions (sectors 1–6) and then considering 4 different socio–economic levels (groups 1–4) among main cities in Eastern regions (sector 2 and 4) where the concentrations level is the highest in China and NO2 concentrations show continued increasing trend. Especially OMI NO2 and emissions consistently showed that metropolitan/big—sized and developed cities (group 1), such as Beijing and Shanghai, had an increasing trend of NO2 concentrations until 2007 and decreasing thereafter, while small/mid–sized and developing cities (groups 2 and 3) kept a continuous increasing trend over all periods. The seasonal change in NO2 concentrations showed the apparent increasing trend in winter and no significant trend in summer in all groups except for group 1. These results indicate that an increase in AMNC in Northeast Asia was mainly attributed to the increasing NO2 concentrations in winter in groups 2 and 3. Therefore, it strongly suggests the importance of the NO2 management for groups 2 and 3 to improve air quality in the Northeast Asia.

This study investigated the year-to-year variability in the occurrence, abundance and sources of oil spills in the Eastern Arabian Sea (EAS) using sentinel-1 imagery and identified the potential oil spills vulnerable zones. The four consecutive year's data acquired from 2017 to 2020 (March–May) reveal three oil spill hot spot zones. The ship-based oil spills were dominant over zone's-1 (off Gujarat) and 3 (off Karnataka and Kerala), and the oil field based over zone-2 (off Maharashtra). The abundance of oil spills was significantly low in zone-1, only 14.30km² (1.2%) during lock-down due to the covid-19 pandemic. Whereas, the year-to-year oil spills over zone's 2 and 3 are not significantly varied (170.29 km² and 195.01 km²), further suggesting the influence of oil exploration and international tanker traffic are in operation during the lock-down. This study further recommends that manual clustering is the best method to study the distribution of unknown oil spills.

A comprehensive, high resolution, ground truthed benthic habitat map has been completed for Qatar's coastal zone and Halul Island. The objectives of this research were to; 1. Systematically compare and contrast pixel- and object-based classifiers for benthic mapping in a limited focus area and then to, 2. Apply these learnings to develop an accurate high resolution benthic habitat map for the entirety of the Qatari coastal zone. Results indicate object-based methods proved more efficient and accurate when compared to pixel based classifiers. The developed country-wide map covers 4500 km² and underscores the complex interplay of seagrass, macroalgal, and reefal habitats, as well as areas of expansive mangrove forests and microbial mats. The map developed here is a first of its kind in the region. Many potential applications exist for the datasets collected to provide fundamental information that can be used for ecosystem-based management decision making.