Urban Heat Island (UHI) is a common urban problem associated with a wide variety of factors, including air pollution. This study investigated the relationship between Land Surface Temperature (LST) and air pollution as two spatial phenomena affecting urban areas. LST was estimated from OLI sensor images taken on 01/07/2020 using the single-channel algorithm. Air pollution was assumed to be indicated by the concentrations of NOX, NO2, NO, PM2.5 and SO2, which were obtained by Inverse Distance Weighting (IDW) interpolation from the data recorded on the same date as satellite images. Correlations were measured in terms of R and R2 and errors were estimated in terms of RMSE, MAE and MBE. The highest R and R2 were obtained for SO2 (20.89 and 45.99, respectively). The results showed that despite the high correlation between SO2 and LST, PM2.5 has a much better error distribution. Therefore, further research should be conducted on the relationship between these indices.

Industrial parks emit large amounts of anthropogenic heat and aggravate the urban heat island effect, which has become a severe environmental problem worldwide. Few studies explored if the warming effect generated by concentrated industrial facilities (i.e., steel plants in this study) produces an intra-heat island effect in urban built-up areas. Sufficient evidence of an industrial heat island (IHI) effect is lacking, and new quantitative methods are urgently needed to address these issues. Therefore, we proposed a new scheme to quantify the warming effect of large, heat-emitting urban objects versus complex surroundings, and the IHI effect was accordingly defined at a finer scale. This study separated the industrial park from other artificial lands and comprehensively estimated the IHI effects' spatiotemporal variation. The IHI intensities were measured based on varied natural and urbanized references, which provided new evidence for the existence of the IHI effect over space and seasons. The land surface temperature (LST) profiles delineated the downward trend in LST variation from inside to surroundings in the IHI cases on both spatial and temporal scales. The time-series analysis revealed that the IHI effects demonstrated more significant disparities regarding the LSTs between the industrial parks and their surrounding backgrounds during warm seasons than in cold seasons. And a more severe IHI effect was observed in spring and summer, and the weakest IHI intensity occurred in winter. Moreover, the IHI intensity is positively associated to the anthropogenic heat, indicating that the industrial activities contribute to the increased LSTs of the industrial park to a great extent. The rationale of the IHI effect can broaden insight for understanding how urban industrial heat sources influence the regional thermal environment, especially at a finer scale.

Wind induced flow velocity patterns and associated thermal destratification can drive to hypoxia reduction in large shallow lakes. The effects of wind induced hydrodynamic changes on destratification and hypoxia reduction were investigated at the Meiling bay (N 31° 22′ 56.4″, E 120° 9′ 38.3″) of Lake Taihu, China. Vertical flow velocity profile analysis showed surface flow velocities consistency with the wind field and lower flow velocity profiles were also consistent (but with delay response time) when the wind speed was higher than 6.2 m/s. Wind field and temperature found the control parameters for hypoxia reduction and for water quality conditions at the surface and bottom profiles of lake. The critical temperature for hypoxia reduction at the surface and the bottom profile was ≤24.1C° (below which hypoxic conditions were found reduced). Strong prevailing wind field (onshore wind directions ESE, SE, SSE and E, wind speed ranges of 2.4–9.1 m/s) reduced the temperature (22C° to 24.1C°) caused reduction of hypoxia at the near surface with a rise in water levels whereas, low to medium prevailing wind field did not supported destratification which increased temperature resulting in increased hypoxia. Non-prevailing wind directions (offshore) were not found supportive for the reduction of hypoxia in study area due to less variable wind field. Daytime wind field found more variable (as compared to night time) which increased the thermal destratification during daytime and found supportive for destratification and hypoxia reduction. The second order exponential correlation found between surface temperature and Chlorophyll-a (R²: 0.2858, Adjusted R-square: 0.2144 RMSE: 4.395), Dissolved Oxygen (R²: 0.596, Adjusted R-square: 0.5942, RMSE: 0.3042) concentrations. The findings of the present study reveal the driving mechanism of wind induced thermal destratification and hypoxic conditions, which may further help to evaluate the wind role in eutrophication process and algal blooms formation in shallow water environments.Wind field is the key control factor for thermal destratification and hypoxia reduction. 24.1C° is the critical/threshold temperature for hypoxia, Chlorophyll-a and NH3-N concentrations of the shallow freshwater lake.

Fine particulate matter (PM₂.₅) has been recognized as a key air pollutant that can influence population health risk, especially during extreme cases such as wildfires. Previous studies have applied geospatial techniques such as land use regression to map the ground-level PM₂.₅, while some recent studies have found that Aerosol Optical Depth (AOD) derived from satellite images and machine learning techniques may be two elements that can improve spatiotemporal prediction. However, there has been a lack of studies evaluating use of different machine learning techniques with AOD datasets for mapping PM₂.₅, especially in areas with high spatiotemporal variability of PM₂.₅.In this study, we compared the performance of eight predictive algorithms with the use of multiple remote sensing datasets, including satellite-derived AOD data, for the prediction of ground-level PM2.5 concentration. Based on the results, Cubist, random forest and eXtreme Gradient Boosting were the algorithms with better performance, while Cubist was the best (CV-RMSE = 2.64 μg/m3, CV-R² = 0.48). Variable importance analysis indicated that the predictors with the highest contributions in modelling were monthly AOD and elevation.In conclusion, appropriate selection of machine learning algorithms can improve ground-level PM2.5 estimation, especially for areas with nonlinear relationships between PM2.5 and predictors caused by complex terrain. Satellite-derived data such as AOD and land surface temperature (LST) can also be substitutes for traditional datasets retrieved from weather stations, especially for areas with sparse and uneven distribution of stations.

Understanding the effects of urban morphology on urban environmental noise (UEN) at a regional scale is crucial for creating a pleasant urban acoustic environment. This study seeks to investigate how the urban morphology influences the UEN in the Shenzhen metropolitan region of China, by employing remote sensing and geographic information data. The UEN in this study consists of not only regional environmental noise (RN), but also traffic noise (TN). The experimental results reveal the following findings: 1) RN is positively correlated with the nighttime light intensity (NTL) and land surface temperature (LST) (p < 0.05). More interestingly, landscape composition and configuration can also significantly affect RN. For instance, urban vegetation can mitigate the RN (r = −0.411, p < 0.01). There is a reduced RN effect when fewer buildings exist in an urban landscape, in terms of the positive relationship between building density and RN (r = 0.188, p < 0.01). Given the same percentage of building area, buildings are more effective at reducing noise when they are distributed across the urban scenes, rather than being spatially concentrated (r = −0.205, p < 0.01). 2) TN positively relates to large (r = 0.520, p < 0.01) and small–medium (r = 0.508, p < 0.01) vehicle flow. In addition, vegetation along or near roads can alleviate the TN effect (r = −0.342, p < 0.01). TN can also become more severe in urban landscapes where there is higher road density (r = 0.307, p < 0.01). 3) Concerning the urban functional zones, traffic land is the greatest contributor to urban RN, followed by mixed residential and commercial land. The findings revealed by this research will indicate how to mitigate UEN.

Urban heat island (UHI) can generate profound impacts on socioeconomics, human life, and the environment. Most previous studies have estimated UHI intensity using outdated urban extent maps to define urban and its surrounding areas, and the impacts of urban boundary expansion have never been quantified. Here, we assess the possible biases in UHI intensity estimates induced by outdated urban boundary maps using MODIS Land surface temperature (LST) data from 2009 to 2011 for China's 32 major cities, in combination with the urban boundaries generated from urban extent maps of the years 2000, 2005 and 2010. Our results suggest that it is critical to use concurrent urban extent and LST maps to estimate UHI at the city and national levels. Specific definition of UHI matters for the direction and magnitude of potential biases in estimating UHI intensity using outdated urban extent maps.

Identifying factors that influence the land surface temperature (LST) of urban forests can help improve simulations and predictions of spatial patterns of urban cool islands. This requires a quantitative analytical method that combines spatial statistical analysis with multi-source observational data. The purpose of this study was to reveal how human activities and ecological factors jointly influence LST in clustering regions (hot or cool spots) of urban forests. Using Xiamen City, China from 1996 to 2006 as a case study, we explored the interactions between human activities and ecological factors, as well as their influences on urban forest LST. Population density was selected as a proxy for human activity. We integrated multi-source data (forest inventory, digital elevation models (DEM), population, and remote sensing imagery) to develop a database on a unified urban scale. The driving mechanism of urban forest LST was revealed through a combination of multi-source spatial data and spatial statistical analysis of clustering regions. The results showed that the main factors contributing to urban forest LST were dominant tree species and elevation. The interactions between human activity and specific ecological factors linearly or nonlinearly increased LST in urban forests. Strong interactions between elevation and dominant species were generally observed and were prevalent in either hot or cold spots areas in different years. In conclusion, quantitative studies based on spatial statistics and GeogDetector models should be conducted in urban areas to reveal interactions between human activities, ecological factors, and LST.

Perennial contaminated groundwater seepage is threatening the downstream ecosystem of the Kazipally Pharmaceutical industrial area located in South India. The sources of seepage are unknown for the last three decades that challenging the regulatory authorities and industries. In general, water quality monitoring and geophysical techniques are applied to identify the sources. However, these techniques may lead to ambiguous results and fail to identify the seepage sources, especially when the area is urbanized/paved, and groundwater is already contaminated with other leakage sources that have similar chemical compounds. In the present study, a novel and multidisciplinary approach were adopted that includes satellite-based Land Surface Temperature (LST) observations, field-based Electrical Resistivity Tomography (ERT), continuous Soil Electrical Conductivity (SEC) and Volumetric Soil Moisture (VSM%) measurements along with groundwater levels monitoring to identify the sources and to control the seepage. The integrated results identified that the locations with the Standard Thermal Anomaly (STA) in the range of −0.5 to -1 °C, VSM% >50%, SEC > 1.5 mS/cm, bulk resistivity < 12 Ω m with shallow groundwater levels < 3 m below ground level (bgl) are potentially contaminated perennial seepage sources. Impermeable sheet piles have been installed across the groundwater flow direction to control the seepage up to 1.5 m bgl, where groundwater frequently intercepts land surface. The quantity of dry season groundwater seepage has been declined by 79.2% after these interventions, which in turn minimized the treatment cost of 1,96,283 USD/year and improved the downstream ecosystem.

In this work, a sand and dust storm vulnerability mapping (SDS-VM) approach is developed to model the vulnerability of urban blocks to SDS using GIS spatial analysis and a range of geographical data. The SDS-VM was carried out in Ahvaz, IRAN, representing one of the most dust-polluted cities in West Asia. Here, vulnerability is defined as a function of three components: exposure, sensitivity, and adaptive capacity of the people in the city blocks to sand and dust storms. These components were formulated into measurable indicators (i.e. GIS layers) including: PM₂.₅, wind speed, distance from dust emission sources, demographic statistics (age, gender, family size, education level), number of building floors, building age, land surface temperature (LST), land use, percentage of literate population, distance from health services, distance from city facilities (city center, shopping centers), distance from infrastructure (public transportation, main roads and highways), distance from parks and green spaces, and green area per capita. The components and the indicators were weighted using analytical hierarchy process (AHP). Different levels of risks for the components and the indicators were defined using ordered weighted averaging (OWA). Urban SDS vulnerability maps at different risk levels were generated through spatial multi-criteria data analysis procedure. Vulnerability maps, with different risk levels, were validated against field-collected data of 781 patients hospitalized for dust-related diseases (i.e. respiratory, cardiovascular, and skin). Results showed that (i) SDS vulnerability map, obtained from the developed methodology, gives an overall accuracy of 79%; (ii); regions 1 and 5 of Ahvaz are recognized with the highest and lowest vulnerabilities to SDS, respectively; and (iii) ORness equal to 0 (very low risk) is the optimum SDS-VM risk level for decision-making to mitigate the harmful impacts of SDS in the deposition areas of Ahvaz city.

Facing the concern of the population to its environment and to climatic change, city planners are now considering the urban climate in their choices of planning. The use of climatic maps, such Urban Climate Zone‑UCZ, is adapted for this kind of application. The objective of this paper is to demonstrate that the UCZ classification, integrated in the World Meteorological Organization guidelines, first can be automatically determined for sample areas and second is meaningful according to climatic variables. The analysis presented is applied on Toulouse urban area (France). Results show first that UCZ differentiate according to air and surface temperature. It has been possible to determine the membership of sample areas to an UCZ using landscape descriptors automatically computed with GIS and remote sensed data. It also emphasizes that climate behavior and magnitude of UCZ may vary from winter to summer. Finally we discuss the influence of climate data and scale of observation on UCZ mapping and climate characterization.