Pollution characteristics and risk assessment of antimony and arsenic in a typical abandoned antimony smelter.

Antimony (Sb) and arsenic (As) co-contamination occurs in Sb smelting areas and is harmful to the surrounding ecological environment. The purpose of this study is to explore the spatial distribution characteristics of Sb and As in abandoned Sb smelting area and carry out risk assessments. Soil samples were collected from the smelting area profile and background points, and groundwater samples were also collected. Samples from two geological background sections were collected to understand the geological background characteristics of Sb and As. The spatial distribution was drawn via the inverse distance weighted interpolation method. The hazard assessment was carried out by the geo-accumulation index and potential ecological hazard methods. The results showed that special high geological background value of Sb and As in study area. Sb and As co-contamination is one of the characters in soil. And the contents of Sb and As decrease as depth increases, reflecting the weak migration capacity. The spatial distribution of Sb and As is affected by slag distribution and rainfall leaching. The Sb content in groundwater was higher in the wet and normal seasons than in the dry season, slag leaching may be one of the elements. The potential ecological hazards of Sb and As are high and considerable, respectively. In abandoned smelting area with high geological background values, it is necessary to focus on the pollution abatement and protection of ecological health.

Appraising regional anthropogenic heat flux using high spatial resolution NTL and POI data: A case study in the Beijing-Tianjin-Hebei region, China.

Rapid urbanization and the aggregation of human activities in cities have resulted in large amounts of anthropogenic heat (AH) emission, which affects urban climate. Quantifying and assessing the AH emission values accurately and analyzing their spatial distribution characteristics is important to understand the energy exchange processes of urban areas. In this study, the high spatial resolution anthropogenic heat flux (AHF) quantification and spatial distribution analysis were conducted using multi-source data in the Beijing-Tianjin-Hebei region (BTH region) of China. First, the AH emission in district and city level were estimated using inventory method based on energy consumption and socio-economic statistical data; Then, AHF spatial quantification models were constructed based on high spatial resolution nighttime light (NTL) data and Point of interests (POI) data, and 130 m × 130 m gridded AHF quantification result in BTH region was realized; Finally, the potential numerical and spatial distribution patterns of AHF were analyzed using various indicators including contribution rate and aggregation index. The results show that: (1) The parameterized index constructed based on NTL and POI data shows a strong correlation with AHF, with R2 ranging from 0.79 to 0.94 and a mean absolute error (MAE) value of 0.72 w·m-2, which can be applied to the quantification of gridded AHF values with high resolution. The highest total AHF in the study area is 214 w·m-2, and the average value is 2.24 w·m-2. (2) Considering the sources of AHF, industrial emission sources in BTH region contribute the most to the total AHF, but commercial building emission sources in Beijing have a higher contribution, which can reach 33.8%. (3) Different types of AHF have different spatial aggregation levels. Commercial building emission and human metabolic emission have the highest aggregation level, and transportation emission has the lowest aggregation level.

Mapping China's time-series anthropogenic heat flux with inventory method and multi-source remotely sensed data.

Mapping time-series anthropogenic heat flux (AHF) is of great significance for understanding the process of urbanization and its impact on urban environment and climate. By collecting energy consumption data and socioeconomic statistics, combined with multi-source remotely sensed data, this study mapped the surface AHF in China with a high spatial resolution of 500 m × 500 m from 2000 to 2016 with 4 years of interval through constructing AHF estimation scheme. The main conclusions are: (1) There is a strong correlation between the vegetation adjusted nighttime light urban index (VANUI) and AHF. The highest coefficient of determination (R2) of VANUI and AHF is 0.97 in partition of northwest region (NWR). The average R2 value in partitions is 0.76, which shows that VANUI can well reflect the spatial differentiation characteristics of anthropogenic heat emissions. In addition, the fitting R2 value of the AHF estimation result and the AHF calculated by the inventory method is between 0.7 and 0.9, which indicates that the AHF estimation model constructed by VANUI can obtain reliable AHF estimation results. (2) In 2000-2016, the composition of AHF value changed a lot. The most obvious change is the AHF of 2-5 W·m-2, with a total increase of 21.53%. The area ratio of the low-value AHF of 0-2 W·m-2 showed a decreasing trend, from 91.93% in 2000 to 50.45% in 2016. Due to the increase of AHF, the reduced area has evolved to a high anthropogenic heat emission area. By constructing the AHF estimation model, this study acquired the time-series AHF with good accuracy and time-variation consistency in China from 2000 to 2016, which can effectively serve the research on urban environment and climate.

Characterizing spatiotemporal dynamics of anthropogenic heat fluxes: A 20-year case study in Beijing-Tianjin-Hebei region in China.

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·â€¯m-2 in 1995 to 1.46 W·â€¯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.