In this study, the Songgang River (SR) was selected as a typical tributary that is heavily polluted by rapid urbanization and industrialization. The polycyclic aromatic hydrocarbon (PAH) distribution at five representative sampling sites from different urban functional areas was studied. The chemical and physical properties and spatial and vertical distribution of PAHs in sediments were investigated. PAH source identification and the ecological risks of the sediments were evaluated. The results suggested that the industrial zone and dense residential and commercial areas were the most contaminated areas of the SR, as the chemical and physical properties of total organic carbon content in sediments was the highest at the dense residential and commercial areas (0.1–4.5%); however, the acid volatile sulfide, total nitrogen, and total phosphorus contents were the highest in the industrial zone, with ranges of 700.0–1618.4 mg/kg dw, 22.4–3543.9 mg/kg dw, and 82.3–4550.7 mg/kg dw, respectively. The spatial distribution of residual PAHs in the sediment cores showed a wide variation among different urban functional areas, and the vertical characterization (0–300 cm) depicted a significant decreasing trend with depth and with an abrupt increase at 180 cm. The concentration of ∑16 PAHs ranged from 208.7 to 7709.8 ng/g dw, with the highest concentrations obtained in the industrial zone. The low molecular weight-PAHs (153–6720 ng/g dw) were predominant in the sediments. Furthermore, there were combined sources (biomass burning: 40.3%; fossil fuel combustion: 25.5%; mixed source: 21.5%; oil pollution: 12.7%) and a long term accumulation effect, with anthropogenic activities and industrial pollution as the major contributing sources. The concentrations of Nap, Acy, Ace, Flu, and Ant exceeded the lower limit of the sediment quality criteria, and higher toxic equivalent concentration values of the total carcinogenic PAHs were observed nearby the midstream of the SR, which may cause adverse biological effects and implies a need for regular monitoring.

An accurate estimation of population exposure to particulate matter with an aerodynamic diameter <2.5 μm (PM₂.₅) is crucial to hazard assessment and epidemiology. This study integrated annual data from 1146 in-home air monitors, air quality monitoring network, public applications, and traffic smart cards to determine the pattern of PM₂.₅ concentrations and activities in different microenvironments (including outdoors, indoors, subways, buses, and cars). By combining massive amounts of signaling data from cell phones, this study applied a spatio-temporally weighted model to improve the estimation of PM₂.₅ exposure. Using Shanghai as a case study, the annual average indoor PM₂.₅ concentration was estimated to be 29.3 ± 27.1 μg/m³ (n = 365), with an average infiltration factor of 0.63. The spatio-temporally weighted PM₂.₅ exposure was estimated to be 32.1 ± 13.9 μg/m³ (n = 365), with indoor PM₂.₅ contributing the most (85.1%), followed by outdoor (7.6%), bus (3.7%), subway (3.1%), and car (0.5%). However, considering that outdoor PM₂.₅ makes a significant contribution to indoor PM₂.₅, outdoor PM₂.₅ was responsible for most of the exposure in Shanghai. A heatmap of PM₂.₅ exposure indicated that the inner-city exposure index was significantly higher than that of the outskirts city, which demonstrated that the importance of spatial differences in population exposure estimation.

Useful information on the potential radon risk in existing buildings can be obtained by combining data from sources such as potential risk maps, the ‘Sistema de Información sobre Ocupación del Suelo de España’ (SIOSE) [information system on land occupancy in Spain], cadastral data on built property and population surveys. The present study proposes a method for identifying urban land, premises and individuals potentially subject to radon risk. The procedure draws from geographic information systems (GIS) pooled at the municipal scale and data on buildings possibly affected. The method quantifies the magnitude of the problem in the form of indicators on the buildings, number of premises and gross floor area that may be affected in each risk category. The findings are classified by type of use: residential, educational or office. That information may guide health/prevention policies by targeting areas to be measured based on risk category, or protection policies geared to the construction industry by estimating the number of buildings in need of treatment or remediation. Application of the methodology to Greater Madrid showed that 47% of the municipalities have houses located in high radon risk areas. Using cadastral data to zoom in on those at highest risk yielded information on the floor area of the vulnerable (basement, ground and first storey) premises, which could then be compared to the total. In small towns, the area affected differed only scantly from the total, given the substantial proportion of low-rise buildings in such municipalities.

As a country with the highest CO2 emissions and at the turning point of socio-economic transition, China's effort to reduce CO2 emissions will be crucial for climate change mitigation. Yet, due to geospatial variations of CO2 emissions in different cities, it is important to develop city-specific policies and tools to help control and reduce CO2 emissions. The key question is how to identify and quantify these variations so as to provide reference for the formulation of the corresponding mitigation policies. This paper attempts to answer this question through a case study of 26 cities in the Yangtze River Delta. The CO2 emissions pattern of each city is measured by two statistics: Gini coefficient to describe its quantitative pattern and Global Moran's I index to capture its spatial pattern. It is found that Gini coefficients in all these cities are all greater than 0.94, implying a highly polarized pattern in terms of quantity; and the maximum value for Global Moran's I index is 0.071 with a standard deviation of 0.021, indicating a weak spatial clustering trend but strong difference among these cities. So, it would be more efficient for these cities at current stage to reduce CO2 emissions by focusing on the large emission sources at certain small localities, particularly the very built-up areas rather than covering all the emission sources on every plot of the urban prefectures. And by a combination of these two metrics, the 26 cities are regrouped into nine types with most of them are subject to type HL and ML. These reclassification results then can serve as reference for customizing mitigation policies accordingly and positioning these policies in a more accurate way in each city.

Acid mine drainage (AMD) is a global environmental issue. Conventionally, a number of active and passive remediation approaches are applied to treat and manage AMD. Case studies on remediation approaches applied in actual mining sites such as lime neutralization, bioremediation, wetlands and permeable reactive barriers provide an outlook on actual long-term implications of AMD remediation. Hence, in spite of available remediation approaches, AMD treatment remains a challenge. The need for sustainable AMD treatment approaches has led to much focus on water reuse and resource recovery. This review underscores (i) characteristics and implication of AMD, (ii) remediation approaches in mining sites, (iii) alternative treatment technologies for water reuse, and (iv) resource recovery. Specifically, the role of membrane processes and alternative treatment technologies to produce water for reuse from AMD is highlighted. Although membrane processes are favorable for water reuse, they cannot achieve resource recovery, specifically selective valuable metal recovery. The approach of integrated membrane and conventional treatment processes are especially promising for attaining both water reuse and recovery of resources such as sulfuric acid, metals and rare earth elements. Overall, this review provides insights in establishing reuse and resource recovery as the holistic approach towards sustainable AMD treatment. Finally, integrated technologies that deserve in depth future exploration is highlighted.

This study was initiated after the appearance of chlorotic and necrotic lesions on vegetation in the vicinity of a glassworks. The aim was to establish whether the cause was an uncontrolled release of gaseous fluorides. Five different plant species (Norway spruce, peach, common hornbeam, common bean, common grape vine) were collected in the influenced area, and the fluorine (F) content was determined by a fluoride ion selective electrode after prior total sample decomposition by alkaline carbonate fusion. The measurement results were reported together with their measurement uncertainties (MUs), which were evaluated according to the Guide to the Expression of Uncertainty in Measurement. The F contents at comparable distances from the emitter and in a clean area, free from natural or anthropogenic fluoride emissions, were 87–676 and 10 μg g⁻¹, respectively, thereby confirming the release of gaseous fluorides from the glassworks. The F contents in samples of Norway spruce taken at various radial distances from the emitter suggest that the emitted gaseous fluorides were spread about evenly in all directions from the source following an inverse-power function. Estimated distances at which the F content would decrease to 50 μg g⁻¹ (allowed maximum content of F in feeding stuffs) and 21 μg g⁻¹ (maximum fluoride content in vegetables and fruits in relation to the upper limit of fluoride intake for humans) were 378 m and 571 m, respectively, from the emitter. Evaluation of our results for compliance with specification revealed a lack of regulation on fluoride content in the diet of humans and animals as well as a lack of guidelines on how to take into account MU.

Flaring is a common and necessary operation for chemical industries, which is designed to manage dangerous process overpressure scenarios or to release and destroy off-spec products during chemical plant upsets or turnarounds. However, excessive flaring can emit large quantities of VOCs and NOx into the atmosphere, which will cause transient and localized ozone pollution events in the presence of sunlight. The objective of this study was to quantify the impact to regional air-quality due to flare emissions from chemical plant start-up operations through the coupling of dynamic process simulations via Aspen Plus and air-quality simulations via CAMx. Simulation results from case studies have indicated that the corresponding ozone increments can vary significantly from 0.2 ppb to 17.8 ppb under different temporal and spatial factors, including the start-up starting hour, starting day, and plant location. Additional ozone sensitivity simulations have also indicated that the corresponding ozone increments are higher when the plant is located in a VOC-limited area than that in a NOx-limited area. The results from this study have delivered a cost-effective air-quality control practice for plant start-ups with a minimum air-quality impact through selecting the optimal starting time within the allowable ranges. The practice has significant potential to benefit all stakeholders, including environmental agencies, chemical industries, and local communities.

Atmospheric aerosols have been found to influence the development of planetary boundary layer (PBL) and hence to aggravate haze pollution in megacities. PBL height (PBLH) determines the vertical extent to which the most pollutant effectively disperses and is a key argument in pollution study. In this study, we quantitatively evaluate aerosol radiation effect on PBL, as well as assessment of surface cooling effect and atmosphere heating effect. All the data are measured at a site of Beijing from 2014 to 2017, of which PBLH is retrieved from micro pulse lidar and aerosol optical depth (AOD) from sunphotometer. Case study shows qualitatively that relative high aerosol load reduces PBLH, and in turn causes a high surface PM₂.₅ concentration. We preliminarily reveal the influential mechanism of aerosol on PBL. The influence of aerosol on the radiation flux of PBL is analyzed, with the correlation coefficient (R) of 0.938 between AOD and radiative forcing of BOA (RFBOA) and R = 0.43 between RFBOA and PBLH. Also, AOD is found to negatively correlate with PBLH (R = −0.41). With the increase of AOD, the cooling effect of surface is enhanced, and further impede the development of PBL. Due to aerosol-induced reduction of PBLH, near surface PM₂.₅ concentration surges and presents an exponential growth following AOD. Then, it is speculated and testified that the relationship between SSA (single scatting albedo) and PBLH would be determined by the location of absorbing aerosol within PBL. The upper PBL absorbing aerosol may decrease PBLH, while the lower absorbing aerosol appear to enhance PBLH. The study probably can provide effective observational evidence for understanding the effect of aerosol on PBL and be a reference of air pollution mitigation in Beijing and its surrounding areas.

Tropospheric ozone in the surface air has become the primary atmospheric pollutant in Hangzhou, China, in recent years. Previous analysis is not enough to decode it for better regulation. Therefore, we use the traditional atmospheric model, Weather Research and Forecasting coupled with Community Multi-scale Air Quality (WRF-CMAQ), and machine learning models, Extreme Learning Machine (ELM), Multi-layer Perceptron (MLP), Random Forest (RF) and Recurrent Neural Network (RNN) to analyze and predict the ozone in the surface air in Hangzhou, China, using meteorology and air pollutants as input. We firstly quantitatively demonstrate that the dew-point deficit, instead of temperature and relative humidity, is the predominant meteorological factor in shaping tropospheric ozone. Urban heat island, daily direct solar radiation time, wind speed and wind direction play trivial role in impacting tropospheric ozone. NO₂ is the primary influential factors both for hourly ozone and daily O₃-8 h due to the titration effect. The most environmental-friendly way to mitigate the ozone pollution is to lower the volatile organic compounds (VOCs) with the highest ozone formation potentials. We deduce that the tropospheric ozone formation process tends to be not only non-linear but also non-smooth. Compared with the traditional atmospheric models, machine learning, whose characteristics are rapid convergence, short calculating time, adaptation of forecasting episodes, small program memory, higher accuracy and less cost, is able to predict tropospheric ozone more accurately.

The occurrence and concentrations of a wide range of organic phosphates (OPEs) in vehicle's exhaust (VPM), ambient air particulate matter (APM), and soil of various urban environments were researched. VPM comes from passenger cars, commercial vehicles, marine and bus engines emitted in New European Driving Cycle tests whereas APM was sampled in several sites of the Upper Silesia region (Poland). APM and VPM collected on filters and soil from the same locations as APM sampling sites were extracted with dichloromethane and extracts analyzed by gas chromatography-mass spectrometry. The OPEs found include aryl phosphates such as triphenyl phosphate (TPhP) and tricresyl phosphate (TCP), alkyl phosphates - triethyl phosphate (TEP), tripropyl phosphate (TPP), tributyl phosphate (TBP) and tri(butoxyethyl)phosphate (TBEP), and alkylchlorinated phosphates including tris-(2-chloroisopropyl) phosphate (TCiPP) and tris(2-chloroethyl) phosphate (TCEP). Occurrence and concentrations of these compounds in the PM investigated are highly variable. It was found that total concentrations in APM are directly related to traffic density in particular sites of the urban environment and a style of a vehicle driving. The highest emission of OPEs was found at a crossroad and city center sites where traffic is the densest and vehicles stops and starts are frequent. Village and residential areas were less exposed to OPEs emission. Since OPEs concentrations show exponential correlations to each other also human exposure to these compounds increases exponentially with increasing traffic density. High TEP and TBP level is tentatively proposed as an indicator of emission from petrol-fueled cars. Concentrations of OPEs in some soil are related to their emission to the air and resistance to degradation of a particular compound since only the most resistant TCiPP and TPhP were identified in soil extracts.