Soil polycyclic aromatic hydrocarbons (PAHs) generated from industrial processes are highly spatially heterologous, with limited quantitative studies on their main influencing factors. The present study evaluated the soil PAHs in three types of industrial parks (a petrochemical industrial park, a brominated flame retardant manufacturing park, and an e-waste dismantling park) and their surroundings. The total concentrations of 16 PAHs in the parks were 340–2.43 × 10³, 26.2–2.63 × 10³, and 394–2.01 × 10⁴ ng/g, which were significantly higher than those in the surrounding areas by 1–2 orders of magnitude, respectively. The highest soil PAH contamination was observed in the e-waste dismantling park. Nap can be considered as characteristic pollutant in the petrochemical industrial park, while Phe in the flame retardant manufacturing park and e-waste dismantling park. Low molecular weight PAHs (2–3 rings) predominated in the petrochemical industrial park (73.0%) and the surrounding area of brominated flame retardant manufacturing park (80.3%). However, high molecular weight PAHs (4–6 rings) were enriched in the other sampling sites, indicating distinct sources and determinants of soil PAHs. Source apportionment results suggested that PAHs in the parks were mainly derived from the leakage of petroleum products in the petroleum manufacturing process and pyrolysis or combustion of fossil fuels. Contrarily, the PAHs in the surrounding areas could have been derived from the historical coal combustion and traffic emissions. Source emissions, wind direction, and local topography influenced the PAH spatial distributions.

The adverse health effects associated with the inhalation and ingestion of naturally occurring radon gas produced during the uranium decay chain mean that there is a need to identify high-risk areas. This study detected radon-prone areas using a geographic information system (GIS)-based probabilistic and machine learning methods, including the frequency ratio (FR) model and a convolutional neural network (CNN). Ten influencing factors, namely elevation, slope, the topographic wetness index (TWI), valley depth, fault density, lithology, and the average soil copper (Cu), calcium oxide (Cao), ferric oxide (Fe₂O₃), and lead (Pb) concentrations, were analyzed. In total, 27 rock samples with high activity concentration index values were divided randomly into training and validation datasets (70:30 ratio) to train the models. Areas were categorized as very high, high, moderate, low, and very low radon areas. According to the models, approximately 40% of the study area was classified as very high or high risk. Finally, the radon potential maps were validated using the area under the receiver operating characteristic curve (AUC) analysis. This showed that the CNN algorithm was superior to the FR method; for the former, AUC values of 0.844 and 0.840 were obtained using the training and validation datasets, respectively. However, both algorithms had high predictive power. Slope, lithology, and TWI were the best predictors of radon-affected areas. These results provide new information regarding the spatial distribution of radon, and could inform the development of new residential areas. Radon screening is important to reduce public exposure to high levels of naturally occurring radiation.

Fine particulate matter (PM₂.₅) is associated with various adverse health outcomes and poses serious concerns for public health. However, ground monitoring stations for PM₂.₅ measurements are mostly installed in population-dense or urban areas. Thus, satellite retrieved aerosol optical depth (AOD) data, which provide spatial and temporal surrogates of exposure, have become an important tool for PM₂.₅ estimates in a study area. In this study, we used AOD estimates of surface PM₂.₅ together with meteorological and land use variables to estimate monthly PM₂.₅ concentrations at a spatial resolution of 3 km² over Taiwan Island from 2015 to 2019. An ensemble two-stage estimation procedure was proposed, with a generalized additive model (GAM) for temporal-trend removal in the first stage and a random forest model used to assess residual spatiotemporal variations in the second stage. We obtained a model-fitting R² of 0.98 with a root mean square error (RMSE) of 1.40 μg/m3. The leave-one-out cross-validation (LOOCV) R² with seasonal stratification was 0.82, and the RMSE was 3.85 μg/m3, whereas the R² and RMSE obtained by using the pure random forest approach produced R² and RMSE values of 0.74 and 4.60 μg/m3, respectively. The results indicated that the ensemble modeling approach had a higher predictive ability than the pure machine learning method and could provide reliable PM₂.₅ estimates over the entire island, which has complex terrain in terms of land use and topography.

As an important form of urban water resource, urban artificial lakes are severely affected by rapid urbanization and interference from human activities. These small lakes are characterized by their unique irregular shape, fragile ecosystem, and relatively closed, stagnant waterbodies. However, few studies have focused on their hydrodynamics and water quality, in particular the restoration methods and mechanisms remaining unclear. The present study applied the MIKE 21 FM model to investigate the effects of water diversion on water quality in a typical urban artificial lake. By considering different flow arrangements, several model scenarios were set up to predict the impacts of water diversion on selected water quality parameter. The results showed that the effectiveness of water diversion was directly related to flow velocity, the relative position to the fresh water inlet, the amount and quality of fresh water and water remaining to be diluted, and the circulation direction of flow field. The inflow–outflow arrangement was the primary factor determining the flow field and NH₃–N variation trends across the lake, and an increased discharge exhibited unequal effects in individual zones. Wind was also important for the formation of flow circulation and pollutant variation. Methods were proposed for enhancing water quality in urban small-scale lakes, including changing the way diversion projects are managed, improving the quality of diverted flow, enhancing flow fluidity, or utilizing wind effects and local topography.

The aerosols generated from electronic cigarettes have a significant impact on the human respiratory system. Understanding the vaporization characteristics and aerosol optical properties of electronic cigarettes is important for assessing human exposure to aerosols. An experimental platform was designed and built to simulate the atomization process of electronic cigarette and detect the laser transmissivity of aerosols. The optical properties of single particles and polydispersed particle system for aerosols in the visible wavelength ranges of 400–780 nm were analyzed based on Mie theory. The results show that a higher heating power supplied by coil results in a larger average vaporization rate of e-liquid. Meanwhile, the steady-state transmissivity of the laser beam for aerosols reduces as the heating power increases. Under the same heating power and puffing topography, the total particulate mass (TPM) of aerosols generated by the e-liquid composed of higher vegetable glycerin (VG) content decreases. The scattering efficiency factor of aerosol particle of electronic cigarette increases with an increase in particle size. The volume scattering coefficients of a polydispersed particle system of aerosols decrease as the incident visible wavelengths increase. A higher VG content in e-liquid results in decreased TPM and particle number concentration of aerosols and increased the volume scattering coefficient in the visible wavelength range. It can explain an interesting phenomenon that a lower TPM and a better visual effect brought by the aerosols generated by the e-liquid with a higher VG content could be observed concurrently. The mass indexes (e.g., TPM, average vaporization rate, average mass concentration) and optical indexes (e.g., volume scattering coefficient, laser transmissivity) are suggested to be used for the comprehensive evaluation of relative amounts of aerosols. The results have potential significances for the objective and quantitative assessments of aerosols generated from electronic cigarettes.

Southwestern China contains the largest and most well-developed karst region in the world, and the potentially toxic elements (PTEs) content in the soils of the region is remarkably high. To explore the internal and external control factors and sources of soil PTEs enrichment in this area and to provide a basis for the treatment of PTE pollution, 113 soil samples were collected from Hengxian County, a karst region in Guangxi Province, southwestern China. The importance of eighteen influencing factors including parent material, weathering, physicochemical properties, topography and human activities were quantitatively analyzed by (partial) redundancy analysis. The sources of PTEs were identified using the Pb isotope ratio and absolute principal component score/multiple linear regression (APCS-MLR) model. The contents of all soil PTEs were higher than the corresponding background values of Guangxi soils. The contents in Cu, Zn, Cd, Hg and Pb were the highest in the soil from carbonate rock. The factor group of geological background and weathering explained 26.5% for the accumulation and distribution of soil PTEs and the influence of physicochemical properties was less than 2% but increased to 25.6% through interaction with weathering. Fe (47.1%), Al (42.1%), Mn (22%), chemical index of alteration (12.8%) and clay (11.9%) were the key factors affecting the soil PTEs, while the influence of human activities was weak. Pb isotope ratio and APCS-MLR classified 62.8–74% of soil PTEs as derived from natural sources, whereas 18.23% and 18.95% were derived from industrial activities and agricultural practice/traffic emissions, respectively. The Pb isotope ratio showed that the natural sources account for up to 90% of the Pb in the soil from carbonate rock, the highest contribution among the studied soils. The results of the study can provide background information on the soil PTEs contamination in the karst areas of China and other areas worldwide.

PM₂.₅ pollution is caused by multiple factors and determining how these factors affect PM₂.₅ pollution is important for haze control. In this study, we modified the geographically weighted regression (GWR) model and investigated the relationships between PM₂.₅ and its influencing factors. Experiments covering 368 cities and 9 urban agglomerations were conducted in China in 2015 and more than 20 factors were considered. The modified GWR coefficients (MGCs) were calculated for six variables, including two emission factors (SO₂ and NO₂ concentrations), two meteorological factors (relative humidity and lifted index), and two topographical factors (woodland percentage and elevation). Then the spatial distribution of MGCs was analyzed at city, cluster, and region scales. Results showed that the relationships between PM₂.₅ and the different factors varied with location. SO₂ emission positively affected PM₂.₅, and the impact was the strongest in the Beijing–Tianjin–Hebei (BTH) region. The impact of NO₂ was generally smaller than that of SO₂ and could be important in coastal areas. The impact of meteorological factors on PM₂.₅ was complicated in terms of spatial variations, with relative humidity and lifted index exerting a strong positive impact on PM₂.₅ in Pearl River Delta and Central China, respectively. Woodland percentage mainly influenced PM₂.₅ in regions of or near deserts, and elevation was important in BTH and Sichuan. The findings of this study can improve our understanding of haze formation and provide useful information for policy-making.

Isoprene, formaldehyde and acetaldehyde are important reactive organic compounds which strongly impact atmospheric oxidation processes and formation of tropospheric ozone. Monsoon meteorology and the topography of Himalayan foothills cause surface emissions to get rapidly transported both horizontally and vertically, thereby influencing atmospheric processes in distant regions. Further in monsoon, Indo-Gangetic Plain is a major rice growing region of the world and daytime hourly ozone can frequently exceed phytotoxic dose of 40 ppb O₃. However, the sources and ambient variability of these compounds which are potent ozone precursors are unknown. Here, we investigate the sources and photochemical processes driving their emission/formation during monsoon season from a sub-urban site at the foothills of the Himalayas. The measurements were performed in July, August and September using a high sensitivity mass spectrometer. Average ambient mixing ratios (±1σ variability) of isoprene, formaldehyde, acetaldehyde, and the sum of methyl vinyl ketone and methacrolein (MVK+MACR), were 1.4 ± 0.3 ppb, 5.7 ± 0.9 ppb, 4.5 ± 2.0 ppb, 0.75 ± 0.3 ppb, respectively, and much higher than summertime values in May. For isoprene these values were comparable to mixing ratios observed over tropical forests. Surprisingly, despite occurrence of anthropogenic emissions, biogenic emissions were found to be the major source of isoprene with peak daytime isoprene driven by temperature (r ≥ 0.8) and solar radiation. Photo-oxidation of precursor hydrocarbons were the main sources of acetaldehyde, formaldehyde and MVK+MACR. Ambient mixing ratios of all the compounds correlated poorly with acetonitrile (r ≤ 0.2), a chemical tracer for biomass burning suggesting negligible influence of biomass burning during monsoon season. Our results suggest that during monsoon season when radiation and rain are no longer limiting factors and convective activity causes surface emissions to be transported to upper atmosphere, biogenic emissions can significantly impact the remote upper atmosphere, climate and ozone affecting rice yields.

Satellite-based aerosol optical depth (AOD) is now comprehensively applied to estimate ground-level concentrations of fine particulate matter (PM2.5). This study aimed to construct the AOD-PM2.5 estimation models over Taiwan. The AOD-PM2.5 modeling in Taiwan island is challenging owing to heterogeneous land use, complex topography, and humid tropical to subtropical climate conditions with frequent cloud cover and prolonged rainy season. The AOD retrievals from the MODerate resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites were combined with the meteorological variables from reanalysis data and high resolution localized land use variables to estimate PM2.5 over Taiwan island from 2005 to 2015. Ten-fold cross validation was carried out and the residuals of the estimation model at various locations and seasons are assessed. The cross validation (CV) R2 based on monitoring stations were 0.66 and 0.66, with CV root mean square errors of 14.0 μg/m3 (34%) and 12.9 μg/m3 (33%), respectively, for models based on Terra and Aqua AOD. The results provided PM2.5 estimations at locations without surface stations. The estimation revealed PM2.5 concentration hotspots in the central and southern part of the western plain areas, particularly in winter and spring. The annual average of estimated PM2.5 concentrations over Taiwan consistently declined during 2005–2015. The AOD-PM2.5 model is a reliable and validated method for estimating PM2.5 concentrations at locations without monitoring stations in Taiwan, which is crucial for epidemiological study and for the assessment of air quality control policy.

We evaluated spatial patterns of mercury (Hg) deposition through analysis of foliage and forest floor samples from 45 sites across Adirondack Park, NY. Species-specific differences in foliar Hg were evident with the lowest concentrations found in first-year conifer needles and highest concentrations found in black cherry (Prunus serotina). For foliage and forest floor samples, latitude and longitude were negatively correlated with Hg concentrations, likely because of proximity to emission sources, while elevation was positively correlated with Hg concentrations. Elemental analysis showed moderately strong, positive correlations between Hg and nitrogen concentrations. The spatial pattern of Hg deposition across the Adirondacks is similar to patterns of other contaminants that originate largely from combustion sources such as nitrogen and sulfur. The results of this study suggest foliage can be used to assess spatial patterns of Hg deposition in small regions or areas of varied topography where current Hg deposition models are too coarse to predict deposition accurately.