In order to evaluate the effect of mining activity on the environment of the Qhorveh mining area in the west of Iran, the geological, ecological and environmental data, related to social development and regional economic status, were used. The geological data included seven sub-indices, such as vegetation coverage, land utilization type, and fault activity; ecological data, with two sub-indices, such as degree of ecological environment recovery; and finally, environmental data, with three sub-indices, such as soil and dust pollutions. These were selected based on the literature and expert opinion which were utilized for environmental pollution and geo-ecological (EPGE) risk assessment of the study site. Remote sensing (RS) image, field sampling, digital elevation map, and data retrieved from different government agencies were used to generate layers for the sub-indices in the geographic information system (GIS) environment. In addition, the analytical hierarchy process (AHP) method was used to determine the weight of sub-indices. Five levels consisting of best, good, middle, poor and worst were used to describe the EPGE risk assessment of the Qhorveh mining area. Results showed that worst and poor levels of EPGE risk are in the east and northeast of the study area where the gold and pumice mines are located while best and good levels of EPGE risk are in its center where the stone mines are located. According to the results of this research, the EPGE risk assessment of the Qhorveh mining area is affected by the environmental pollution index with its highest weight (0.3908). It can be concluded that the integration of the RS, GIS and AHP methods proposed in this study improved the evaluation quality of EPGE risk assessment.

Crops are prone to various types of stress, such as caused by heavy metals, drought and pest/disease, during their life cycle. Heavy metal stress in crops poses a serious threat to crop quality and human health. However, differentiating between heavy metal and non-heavy metal stress presents a great challenge, since responses to environmental stress in crops are complex and uncertain, with different stressors possibly triggering similar canopy reflectance responses. This study aims to infer the occurrence probability of heavy metal stress (i.e., Cd stress) on a regional scale by integrating satellite-derived vegetation index and spatio-temporal characteristics of different stressors with a Bayesian method. The study area is located in the Hunan Province, China. Seven scenes of Sentinel-2 satellite images from 2016 to 2017 were collected, as well as Cd concentrations in the soil. First, the probability of rice being stressed was screened using the normalized difference red-edge index (NDRE) at all the growth stages of rice. Further, the stressed rice was used as input, along with the coefficients of spatio-temporal variation (CSTV) derived from NDRE, for a Bayesian method to infer rice exposed to Cd pollution. The results demonstrated that NDRE was a sensitive indicator for assessing stress levels in rice crops. The CSTV with a threshold of 2.7 successfully detected rice under Cd as well as abrupt stress on a regional scale. A high map accuracy for Cd induced stress in rice was achieved with an accuracy of 81.57%. This study suggests that vegetation index obtained from satellite images can assist in capturing crop stress, and that the used Bayesian method can be very useful for distinguishing a specific stressor in crops by incorporating temporal-spatial characteristic of different stressors in crops into satellite-derived vegetation index.

Methane is a potent greenhouse gas whose atmospheric dispersion may have different implications at distinct scales. One significant contributor to methane emissions is sugarcane farming in tropical areas like in Mexico, which has the sixth highest production level in the world. A consequence of the industrial use of this resource is that sugarcane preharvest burning emits large quantities of methane and other pollutants. The objective of this research is to estimate the methane emissions by sugarcane burning and to analyze their atmospheric dispersion under the influence of meteorological parameters, according to different concentration scenarios generated during a period. The methane emissions were investigated using the methodology of Seiler and Crutzen, based on the stage production during the harvest periods of 2011/2012, 2012/2013 and 2013/2014. Average of total emissions (1.4 × 103 Mg) at the national level was comparable in magnitude to those of other relevant sugarcane-producing countries such as India and Brazil. Satellite images and statistical methods were used to validate the spatial distribution of methane, which was obtained with the WRF model. The results show a dominant wind circulation pattern toward the east in the San Luis Potosi area, to the west in Jalisco, and the north in Tabasco. In the first two areas, wind convergence at a certain height causes a downward flow, preventing methane dispersion. The concentrations in these areas varied from 9.22 × 10−5 to 1.22 × 102 ppmv and 32 × 10−5 to 2.36 × 102 ppmv, respectively. Wind conditions in Tabasco contributed to high dispersion and low concentrations of methane, varying from 8.74 × 105 to 0.33 × 102 ppmv. Methane is a potent greenhouse gas for which it is essential to study and understand their dispersion at different geographic locations and atmospheric conditions.

In the past few decades, extensive epidemiological studies have focused on exploring the adverse effects of PM₂.₅ (particulate matters with aerodynamic diameters less than 2.5 μm) on public health. However, most of them failed to consider the dynamic changes of population distribution adequately and were limited by the accuracy of PM₂.₅ estimations. Therefore, in this study, location-based service (LBS) data from social media and satellite-derived high-quality PM₂.₅ concentrations were collected to perform highly spatiotemporal exposure assessments for thirteen cities in the Beijing-Tianjin-Hebei (BTH) region, China. The city-scale exposure levels and the corresponding health outcomes were first estimated. Then the uncertainties in exposure risk assessments were quantified based on in-situ PM₂.₅ observations and static population data. The results showed that approximately half of the population living in the BTH region were exposed to monthly mean PM₂.₅ concentration greater than 80 μg/m³ in 2015, and the highest risk was observed in December. In terms of all-cause, cardiovascular, and respiratory disease, the premature deaths attributed to PM₂.₅ were estimated to be 138,150, 80,945, and 18,752, respectively. A comparative analysis between five different exposure models further illustrated that the dynamic population distribution and accurate PM₂.₅ estimations showed great influence on environmental exposure and health assessments and need be carefully considered. Otherwise, the results would be considerably over- or under-estimated.

An accurate estimation of PM2.5 (fine particulate matters with diameters ≤ 2.5 μm) concentration is critical for health risk assessment and generating air pollution control strategies. In this study, a hybrid remote sensing and machine learning approach, named RSRF model is proposed to estimate daily ground-level PM2.5 concentrations, which integrates Random Forest (RF), one of machine learning (ML) models, and aerosol optical depth (AOD), one of remote sensing (RS) products. The proposed RSRF model provides an opportunity for an adequate characterization of real-time spatiotemporal PM2.5 distributions at uninhabited places and complex surfaces. It also offers advantages in handling complicated non-linear relationships among a large number of meteorological, environmental and air pollutant factors, as well as ever-increasing environmental data sets. The applicability of the proposed RSRF model is tested in the Beijing-Tianjin-Hebei region (BTH region) during 2015–2017. Deep Blue (DB) AOD from Aqua-retrieved Collection 6.1 (C_61) aerosol products of Moderate Resolution Imaging Spectroradiometer (MODIS) is validated with Aerosol Robotic Network. The validation results indicate C_61 DB AOD has a high correlation with ground based AOD in the BTH region. The proposed RSRF model performed well in characterizing spatiotemporal variations of annual and seasonal PM2.5 concentrations. It not only is useful to quantify the relationships between PM2.5 and relevant factors such as DB AOD, meteorological and air pollutant variables, but also can provide decision support for air pollution control at a regional environment during haze periods.

Rapid urbanization, which is closely related to economic growth, human health, and micro-climate, has resulted in a considerable amount of anthropogenic heat emissions. The lack of estimation data on long-term anthropogenic heat emissions is a great concern in climate and urban flux research. This study estimated the annual average anthropogenic heat fluxes (AHFs) in Beijing–Tianjin–Hebei region in China between 1995 and 2015 on the basis of multisource remote sensing images and ancillary data. Anthropogenic heat emissions from different sources (e.g., industries, buildings, transportation, and human metabolism) were also estimated to analyze the composition of AHFs. The spatiotemporal dynamics of long-term AHFs with high spatial resolution (500 m) were estimated by using a refined AHF model and then analyzed using trend and standard deviation ellipse analyses. Results showed that values in the region increased significantly from 0.15 W· m−2 in 1995 to 1.46 W· m−2 in 2015. Heat emissions from industries, transportation, buildings, and human metabolism accounted for 64.1%, 17.0%, 15.5%, and 3.4% of the total anthropogenic heat emissions, respectively. Industrial energy consumption was the dominant contributor to the anthropogenic heat emissions in the region. During this period, industrial heat emissions presented an unstable variation but showed a growing trend overall. Heat emissions from buildings increased steadily. Spatial distribution was extended with an increasing tendency of the difference between the maximum and the minimum and was generally dominated by the northeast–southwest directional pattern. The spatiotemporal distribution patterns and trends of AHFs could provide vital support on management decision in city planning and environmental monitoring.

Size-segregated ambient particles down to particles smaller than 0.1 μm (PM₀.₁) were collected during the year 2014–2015 using cascade air samplers with a PM₀.₁ stage, at two cities in Thailand, Bangkok and Chiang Mai. Their characteristics and seasonal behavior were evaluated based on the thermal/optical reflectance (IMPROVE_TOR) method. Diagnostic indices for their emission sources and the black carbon (BC) concentration were assessed using an aethalometer and related to the monthly emission inventory (EI) of particle-bound BC and organic carbon (OC) in order to investigate the contribution of agricultural activities and forest fires as well as agro-industries in Thailand. Monthly provincial EIs were evaluated based on the number of agricultural crops produced corresponding to field residue burning and the use of residues as fuel in agro-industries, and also on the number of hot spots from satellite images corresponding to the areas burned by forest fires. The ratio of char-EC/soot-EC describing the relative influence of biomass combustion to diesel emission was found to be in agreement with the EI of BC from biomass burning in the size range <1 μm. This was especially true for PM₀.₁, which usually tends to be indicative of diesel exhaust particles, and was shown to be very sensitive to the EI of biomass burning. In Chiang Mai, the northern part of Thailand, the forest fires located upwind of the monitoring site were found to be the largest contributor while the carbon behavior at the site in Bangkok was better accounted for by the EI of provinces in central Thailand including Bangkok and its surrounding provinces, where the burning of crop residues and the cultivation of sugarcane for sugar production are significant factors. This suggests that the influence of transportation of polluted air masses is important on a multi-provincial scale (100–200 km) in Thailand.

In recent years, ozone pollution has become more and more serious in China. Several epidemiological studies have demonstrated the correlation between short-term ozone exposure and several health risks including all-cause mortality, cardiovascular mortality, and respiratory mortality. In this study, the daily ozone exposure levels with 10 km × 10 km resolution were estimated based on satellite data derived from Ozone Monitoring Instrument (OMI) and the monitoring data. The health impacts for potential decrease in the daily ozone concentration and the corresponding economic benefits in 2016 were estimated by applying the environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) model. By reducing the daily maximum 8-h average concentration of ozone to 100 μg/m³, the estimated avoided all-cause mortalities were 120 × 10³ (95% confidence interval (CI): 67 × 10³, 160 × 10³) cases and the correspondingly economic benefits ranged from 36 to 64 billion CNY using amended human capital (AHC) and willingness to pay (WTP) method in 2016. If the daily maximum 8-h average concentration of ozone were rolled back to 70 μg/m³, the estimated avoided all-cause mortalities were 160 × 10³ (95% CI: 98 × 10³, 230 × 10³) cases and economic benefits ranged from 54 to 95 billion CNY based on AHC and WTP methods.

Satellite aerosol products have been widely used to retrieve ground PM₂.₅ concentration because of their wide coverage and continuous spatial distribution. While more and more studies have focused on the retrieval algorithms, the foundation for the retrieval—relationship between PM₂.₅ and satellite aerosol optical depth (AOD) has not been fully investigated. In this study, the relationships between PM₂.₅ and AOD were investigated in 368 cities in mainland China from February 2013 to December 2017, at different temporal and regional scales. Pearson correlation coefficients and the PM₂.₅/AOD ratio were used as indicators. Firstly, we established the relationship between PM₂.₅ and AOD in terms of the spatio-temporal variations, and discuss the impact of some potential factors for a better understanding of the spatio-temporal variations. Spatially, we found that the correlation is higher in the Beijing-Tianjin-Hebei and Chengyu regions and weaker in coastal areas. The PM₂.₅/AOD ratio shows an obvious north–south difference, with the ratio in North China higher than South China. Temporally, the correlation coefficient tends to be higher in May and September, with the PM₂.₅/AOD ratio higher in winter and lower in summer. As for interannual variations, we detected a decreasing tendency for the PM₂.₅-AOD correlation and PM₂.₅/AOD ratio for recent years. Then, to determine the impact of the weakening of the PM₂.₅-AOD relationship on PM₂.₅ remote sensing retrieval performance, a geographically weighted regression (GWR) retrieval experiment was conducted. The results showed that the performance of retrievals is also decreasing while PM₂.₅-AOD relationship getting weaker. Our study investigated the PM₂.₅-AOD relationship over a large extent at the city scale, and investigated the temporal variations in terms of interannual variations. The results will be useful for the satellite retrieval of PM₂.₅ concentration and will help us to further understand the PM₂.₅ pollution situation in mainland China.

Eutrophication of lakes has become one of the world's most serious environmental problems, resulting in an urgent need to monitor and provide safeguards to control water quality. Results from analysis of lake trophic status based on calculated throphic state index (TSI) showed that 69.5% of the surveyed 277 lakes were in a state of eutrophication. Significant logarithmic relationships between light absorption of optically active components (aOACs) and TSI (R2 = 0.78) existed: TSI = 13.64 × ln(aOACs)+43.24, and the regression relationship between aOACs and TSI had a better degree of fit (R2) than the currently used reflectance-TSI relationship. aOACs appeared to be a good predictor of TSI estimation in lake ecosystems. The relationship coefficient (aOACs-TSI) slightly varied with lake type, and relationships in saline lakes and phy-type lakes were shown to be more robust than the relationship with the total lake data. This study highlights the quantification of the trophic status in lakes using aOACs, which realized the monitoring of trophic status in lakes using inherent optical properties on a large-scale. To our knowledge this is the first investigation to assess the variability of trophic status in lakes across China. The assessment trophic state of lakes based on aOACs provides a new way to monitor the trophic status of lakes, and findings may have applications for monitoring large-scale and long-term trophic patterns in lakes using remote sensing techniques.