High energy consumption in the urban environment impacts the urban surface energy budget and causes the emission of anthropogenic heat fluxes (AHFs) into the atmosphere. AHFs vary over time and space. Thus, a reliable estimation of AHF is needed for mesoscale meteorological modeling. This study used a statistical regression method to estimate the annual mean gridded AHF with high spatial (1-km) resolution. Compared with current methods for AHF estimation, the statistical regression method is straightforward and can be easily incorporated with meteorological modeling. AHF of the highly populated urban areas in Taiwan were estimated using data from the anthropogenic pollutant emission inventory of CO and NOx for year 2010. Over 40% of the total AHF values in Taiwan main island fell within the range of 10–40 Wm−2. When the study domain was confined to urban land, the percentage contributions from AHF values were increased, with over 68% of the total AHF values within the range of 10–40 Wm−2. AHF values > 40 Wm−2 were more abundant in the Southern region, followed by the Central and Northern regions. An assessment of the heat emissions by the large scale urban consumption of energy (LUCY) model revealed that the mean AHFs are reasonably close to those produced while the maximum AHFs are underestimated. The results obtained evidence the impact of spatial distribution of land use types, particularly population densities, main highways and industries on AHF generation in Taiwan.

Simulation procedure integrated with multi-scale in horizontally regional–urban-point levels and in vertically atmosphere–surface–unsaturated–saturated layers, was newly developed in order to predict the effect of urban geometry and anthropogenic exhaustion on the hydrothermal changes in the atmospheric/land and the interfacial areas of the Japanese megalopolis. The simulated results suggested that the latent heat flux in new water-holding pavement (consisting of porous asphalt and water-holding filler made of steel by-products based on silica compound) has a strong impact on hydrologic cycle and cooling temperature in comparison with the observed heat budget. We evaluated the relationship between the effect of groundwater use as a heat sink to tackle the heat island and the effect of infiltration on the water cycle in the urban area. The result indicates that effective management of water resources would be powerful for ameliorating the heat island and recovering sound hydrologic cycle there.

Anthropogenic heat emission (AHE) is an important driver of urban heat islands (UHIs). Further, both urban thermal environment research and sustainable development planning require an efficient estimation of anthropogenic heat flux (AHF). Therefore, this study proposed an improved multi-source AHF model, which was constructed using diverse data sources and small-scale samples, to better represent the spatiotemporal distribution of AHF. The performances of three machine learning algorithms (Cubist, gradient boosting decision tree, and simple linear regression) were quantitatively evaluated, and the impact of spatiotemporal heterogeneity on AHF estimation was considered for the first time. The results showed that multi-source datasets and sophisticated algorithms could more effectively reduce the estimation error and improve the accuracy of the spatiotemporal distribution of AHF than simple linear regression. In practical applications, the Cubist model performed better, with prediction errors being less than 0.9 W⋅m−2. Further, the characteristics of different heat sources from the model outputs varied widely, and the building metabolic heat exhibited significant seasonal spatiotemporal variations, which were largely determined by the regional climate. In contrast, industrial and transportation heat showed marginal monthly fluctuations. Similarly, spatiotemporal heterogeneity significantly affected the estimation of building metabolic heat (0.62 W⋅m−2), but it did not affect other heat sources. The proposed improved AHF model was verified to effectively capture the spatiotemporal variations of building heat and solve the issue of overestimation of industrial heat in urban regions. This study provides new methods and ideas for the accurate spatiotemporal quantification of AHF that can supplement future studies on climate warming, UHI, and air pollution.

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.

Rapid urbanization and industrialization in China stimulated the great increase of energy consumption, which leads to drastic rise in the emission of anthropogenic waste heat. Anthropogenic heat emission (AHE) is a crucial component of urban energy budget and has direct implications for investigating urban climate and environment. However, reliable and accurate representation of AHE across China is still lacking. This study presented a new machine learning-based top–down approach to generate a gridded anthropogenic heat flux (AHF) benchmark dataset at 1 km spatial resolution for China in 2010. Cubist models were constructed by fusing points-of-interest (POI) data of varying categories and multisource remote sensing data to explore the nonlinear relationships between various geographic predictors and AHE from different heat sources. The strategy of developing specific models for different components and exploiting the complementary features of POIs and remote sensing data generated a more reasonable distribution of AHF. Results showed that the AHF values in urban centers of metropolises over China range from 60 to 190 W m⁻². The highest AHF values were observed in some heavy industrial zones with value up to 415 W m⁻². Compared with previous studies, the spatial distribution of AHF from different heating components was effectively distinguished, which highlights the potential of POI data in improving the precision of AHF mapping. The gridded AHF dataset can serve as input of urban numerical models and can help decision makers in targeting extreme heat sources and polluters in cities and making differentiated and tailored strategies for emission mitigation.

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.

Riverine input is an important source of contaminants in the marine environments. Based on a hydrodynamic model, the dilution characteristics of riverine contaminants in the Seto Inland Sea and their controlling factors were studied. Results showed that contaminant concentration was high in summer and low in winter. Contaminant concentration decreased with the reduction of its half-life period, and the relationship between them followed power functions. Sensitivity experiments suggested that the horizontal current and vertical stratification associated with air-sea heat flux controlled the seasonal cycle of contaminant concentration in the water column; however, surface wind velocity was the dominant factor affecting the surface contaminant concentration. In addition, contaminant concentration in a sub-region was likely controlled by the variations in river discharges close to the sub-region. These results are helpful for predicting contaminant concentrations in the sea and are expected to contribute to assessing the potential ecological risks to aquatic organisms.

To evaluate rate of migration from plastic debris, desorption of model hydrophobic organic chemicals (HOCs) from polyethylene (PE)/polypropylene (PP) films to water was measured using PE/PP films homogeneously loaded with the HOCs. The HOCs fractions remaining in the PE/PP films were compared with those predicted using a model characterized by the mass transfer Biot number. The experimental data agreed with the model simulation, indicating that HOCs desorption from plastic particles can generally be described by the model. For hexachlorocyclohexanes with lower plastic-water partition coefficients, desorption was dominated by diffusion in the plastic film, whereas desorption of chlorinated benzenes with higher partition coefficients was determined by diffusion in the aqueous boundary layer. Evaluation of the fraction of HOCs remaining in plastic films with respect to film thickness and desorption time showed that the partition coefficient between plastic and water is the most important parameter influencing the desorption half-life.

A three dimensional rectangular grid model is applied to resolve the temperature–salinity dynamics of Ruwais, a segment of the UAE coast which is well known as dense water formation zone. The model employs a heat flux module and a turbulence closure scheme that facilitate realistic calculation of temperature–salinity dynamics. A field survey campaign is carried out to support the modeling study, involving measurements of tide, currents, temperature, and salinity. Investigation is done for two meteorologically extreme conditions, i.e. summer and winter. The model study showed that the western flux develops an anticlockwise circulation in the study area. The water industrial discharges elevated the temperature and salinity of the water near the southeastern shoreline. This water mass propagated towards north under the influence of gravity.

We investigated the physical properties of beaches contaminated with plastic fragments. We compared sediment cores from Hawai‘i Island’s Kamilo Beach, notable for plastic accumulation, to cores from a nearby beach. Compared to the nearby beach, Kamilo sediments contained more plastics (up to 30.2% by weight), were coarser-grained, and were more permeable (t-test, p<0.0001). 85% of the fragments were polyethylene, and 95% were concentrated in the top 15cm of the cores. We constructed artificial cores of standardized grain size and varying plastic-to-sediment ratios. Adding plastic significantly increased the permeability (ANOVA, p=0.002), which was partially attributed to the fragments increasing the mean grain size. Sediments with plastic warmed more slowly (16% maximum decrease in thermal diffusivity), and reached lower maximum temperatures (21% maximum increase in heat capacity). These changes have a variety of potential effects on beach organisms, including those with temperature-dependent sex-determination such as sea turtle eggs.