Exposure to fine particulate matter (PM₂.₅) remains a worldwide public health issue. However, epidemiological studies on the chronic health impacts of PM₂.₅ in the developing countries are hindered by the lack of monitoring data. Despite the recent development of using satellite remote sensing to predict ground-level PM₂.₅ concentrations in China, methods for generating reliable historical PM₂.₅ exposure, especially prior to the construction of PM₂.₅ monitoring network in 2013, are still very rare. In this study, a high-performance machine-learning model was developed directly at monthly level to estimate PM₂.₅ levels in North China Plain. We developed a random forest model using the latest Multi-angle implementation of atmospheric correction (MAIAC) aerosol optical depth (AOD), meteorological parameters, land cover and ground PM₂.₅ measurements from 2013 to 2015. A multiple imputation method was applied to fill the missing values of AOD. We used 10-fold cross-validation (CV) to evaluate model performance and a separate time period, January 2016 to December 2016, was used to validate our model's capability of predicting historical PM₂.₅ concentrations. The overall model CV R² and relative prediction error (RPE) were 0.88 and 18.7%, respectively. Validation results beyond the modeling period (2013–2015) shown that this model can accurately predict historical PM₂.₅ concentrations at the monthly (R² = 0.74, RPE = 27.6%), seasonal (R² = 0.78, RPE = 21.2%) and annual (R² = 0.76, RPE = 16.9%) level. The annual mean predicted PM₂.₅ concentration from 2013 to 2016 in our study domain was 67.7 μg/m3 and Southern Hebei, Western Shandong and Northern Henan were the most polluted areas. Using this computationally efficient, monthly and high-resolution model, we can provide reliable historical PM₂.₅ concentrations for epidemiological studies on PM₂.₅ health effects in China.

Heavy metal pollution can decrease the soil microbial biomass and significantly alter microbial community structure. In this study, a long-term field experiment (5 years) and short-term laboratory experiment (40 d) were employed to evaluate the effects of heavy metals (Cd, Cu, Zn), and their combinations at different concentrations, on the soil microbial biomass and the bacterial community. The ranges of heavy metal concentration in the long-term and short-term experiments were similar, with concentration ranges of Cd, Cu and Zn of about 0.3–1.5, 100–500, and 150–300 mg kg⁻¹, respectively. Microbial biomass decreased with increasing soil heavy metal concentrations in both the long-term and short-term experiments. The interaction between soil physicochemical factors (pH, TN, TC) and heavy metals (Cd, Cu, Zn) played a major role in change in the bacterial community in long-term polluted soil. In the laboratory experiment, although each heavy metal had an adverse effect on the microbial biomass and community structure, Cu appeared to have a greater role in the changes compared to Cd and Zn. However, the synergistic effects of the heavy metals were greater than those of the single metals and the synergistic effect between Cu and Cd was greater than that of Cu and Zn.

To explore the effect of elevated CO₂ concentrations ([CO₂]) on phosphine formation in paddy fields, the matrix-bound phosphine (MBP) content, different phosphorus fractions and various carbon forms in soil samples from rice cultivation under varying CO₂ concentrations of 400 ppm, 550 ppm and 700 ppm by indoor simulation experiment were determined. This study showed that MBP concentration did not increase significantly with elevated [CO₂] over four-week cultivation periods of rice seedlings, regardless of soil layers. MBP had a significant positive correlation with total phosphorus (TP) and inorganic phosphorus (IP), and multiple stepwise linear regression analysis further indicated that MBP preservation in neutral paddy soils with depths of 0–20 cm may have been due to conversion from FeP and CaP. Based on redundancy analysis and forward selection analysis, speculated that the formation of MBP in the neutral paddy soils as the response to atmospheric elevated [CO₂] was due to two processes: (i) FeP transformation affected by the changes of soil respiration (SCO₂) and TOC was the main precursor for the production of MBP; and (ii) CaP transformation resulting from variation in HCO₃⁻ was the secondary MBP source. The complex combination of these two processes is simultaneously controlled by SCO₂. In a word, the soil environment in the condition of elevated [CO₂] was in favor of MBP storage in neutral paddy soils. The results of our study imply that atmospheric CO₂ participates in and has a certain impact on the global biogeochemical cycle of phosphorus.

Effects of photochemical and microbial degradation on variations in composition and molecular-size of dissolved organic matter (DOM) from different sources (algal and soil) and the subsequent influence on Cu(II) binding were investigated using UV–Vis, fluorescence excitation-emission matrices coupled with parallel factor analysis, flow field-flow fractionation (FlFFF), and metal titration. The degradation processes resulted in an initial rapid decline in the bulk dissolved organic carbon and chromophoric and fluorescent DOM components, followed by a small or little decrease. Specifically, photochemical reaction decreased the aromaticity, humification and apparent molecular weights of all DOM samples, whereas a reverse trend was observed during microbial degradation. The FlFFF fractograms revealed that coagulation of both protein- and humic-like DOM induced an increase in molecular weights for algal-DOM, while the molecular weight enhancement for allochthonous soil samples was mainly attributed to the self-assembly of humic-like components. The Cu(II) binding capacity of algal-derived humic-like and fulvic-like DOM consistently increased during photo- and bio-degradation, while the soil-derived DOM exhibited a slight decline in Cu(II) binding capacity during photo-degradation but a substantial increase during microbial degradation, indicating source- and degradation-dependent metal binding heterogeneities. Pearson correlation analysis demonstrated that the Cu(II) binding potential was mostly related with aromaticity and molecular size for allochthonous soil-derived DOM, but was regulated by both DOM properties and specific degradation processes for autochthonous algal-derived DOM. This study highlighted the coupling role of inherent DOM properties and external environmental processes in regulating metal binding, and provided new insights into metal-DOM interactions and the behavior and fate of DOM-bound metals in aquatic environments.

Environmental contamination of the Arctic has widely been used as a worldwide pollution marker. Various classes of organic pollutants such as pesticides, personal care products, PAHs, flame retardants, biomass burning markers, and many others emerging contaminants have been regularly detected in Arctic samples. Although numerous papers have been published reporting data from the Canadian, Danish, and Norwegian Arctic regions, the environmental situation in Russian Arctic remains mostly underreported. Snow analysis is known to be used for monitoring air pollution in the regions with cold climate in both short-term and long-term studies. This paper presents the results of a nontargeted study on the semivolatile organic compounds detected and identified in snow samples collected at the Russian Artic Archipelago Novaya Zemlya in June 2016. Gas chromatography coupled to a high-resolution time-of-flight mass spectrometer enabled the simultaneous detection and quantification of a variety of pollutants including those from the US Environmental Protection Agency (EPA) priority pollutants list, emerging contaminants (plasticizers, flame retardants-only detection), as well as the identification of novel Arctic organic pollutants, (e.g., fatty acid amides and polyoxyalkanes). The possible sources of these novel pollutants are also discussed.GC-HRMS enabled the detection and identification of emerging contaminants and novel organic pollutants in the Arctic, e.g., fatty amides and polyoxyalkanes.

Rapid development of industrial production has stimulated the growth of consumption of raw and auxiliary materials including organic paints, among which volatile organic compounds (VOCs) are proved harmful to the population who inhale the polluted air based on epidemiologic studies. Therefore, new types of environment-friendly paints were developed to replace solvent-based paints (SBPs). Nevertheless, new types of paints containing VOCs failed to replace SBPs entirely due to certain disadvantages. Hence, five kinds of paints were employed in simulation experiments to assess the health risk of primary receptor including three kinds of water-based paints (WBPs) and two kinds of SBPs. Conclusions showed that mean TVOC concentration in breathing zone of primary receptor ranged from 9.5 to 13.6 mg/m³ and 3.4 × 10³ to 1.4 × 10⁴ mg/m³ for WBPs and SBPs, respectively. Assessments of non-cancer risk concluded that nearly one third quantified compounds exceeded corresponding thresholds for WBPs, and the maximum risk value was 101.33; for SBPs, the maximum risk value reached 50760.20, and twenty-two compounds exceeded the reference limits. The calculation of cancer risk values showed that seventeen compounds were higher than acceptable limit amongst which 1,2-dibromoethane had maximum values of 1.27 × 10⁻² to 3.24 × 10⁻² for WBPs; for SBPs, all quantified compounds exceeded the acceptable limit, and 82.61% VOCs were distributed in a scope larger than 1 × 10⁻³. Additionally, a removal efficiency of 60% was considered for primary receptor with personal protective equipment, and subsequent results confirmed its inability of lowering the risk resulted from hazardous VOCs. The calculated potential health risk could be applied to estimate the total health risk for both primary and secondary receptor based on consumed materials. The finding suggested that WBPs could improve VOCs exposure condition and reduce the direct and potential health risk significantly for primary receptor, although they might dissatisfy acceptable limit.

A novel mobile laser radar system was used for mapping gaseous atomic mercury (Hg0) atmospheric pollution in the Wanshan district, south of Tongren City, Guizhou Province, China. This area is heavily impacted by legacy mercury from now abandoned mining activities. Differential absorption lidar measurements were supplemented by localized point monitoring using a Lumex RA-915M Zeeman modulation mercury analyzer. Range-resolved concentration measurements in different directions were performed. Concentrations in the lower atmospheric layers often exceeded levels of 100 ng/m3 for March conditions with temperature ranging from 5 °C to 20 °C. A flux measurement of Hg0 over a vertical cross section of 0.12 km2 resulted in about 29 g/h. Vertical lidar sounding at night revealed quickly falling Hg0 concentrations with height. This is the first lidar mapping demonstration in a heavily mercury-polluted area in China, illustrating the lidar potential in complementing point monitors.

Residues of pyrethroid insecticides tend to accumulate in bed sediments due to their strong hydrophobicity. Rather than the total or bulk sediment concentration, it is the freely dissolved concentration (Cfᵣₑₑ) that drives toxicity to benthic invertebrates. In this study we developed thin film-based samplers for in situ ambient monitoring of pyrethroids at trace levels in sediment. Out of five common polymer materials, polyethylene (PE) and silicone rubber (SR), were identified to offer superior enrichment for pyrethroids from sediment. To circumvent the slow equilibrium process, ¹³C-permethrin and bifenthrin-d₅ were preloaded onto the films as performance reference compounds (PRCs). The PRC-preloaded film samplers were deployed at five sites in Southern California under field conditions for 7 d and retrieved for analysis. The sediment porewater Cfᵣₑₑ of eight pyrethroids derived from PRC-PE films ranged from 173 to 903 ng/L, accounting for 18.2–36.1% of the corresponding total porewater concentrations. The PRC-SR film samplers yielded Cfᵣₑₑ values closely mimicking those from the PRC-PE samplers, cross-validating the two sampling devices. Additionally, a significant positive association was found between the observed mortality from toxicity tests using Hyalella azteca and the Cfᵣₑₑ of bifenthrin (r = 0.628, p = 0.02). A significant linear correlation (R² = 0.99) between Cfᵣₑₑ derived from in situ monitoring and that of ex situ measurement under equilibrium conditions was also observed. Results from this study demonstrated that the film-based samplers may be used for in situ ambient monitoring to detect biologically relevant contamination of pyrethroids in bed sediments, which may contribute to improved risk assessment for this class of widely used insecticides.

The state of practice for noise assessment utilizes established standards for emission and propagation modelling of linear and point sources. Recently, land use regression (LUR) modelling has emerged as an alternative method due to relatively low data and computing resource demands. However, a limitation of LUR modelling is that is does not account for noise attenuation and reflections by features of the built environment. This study demonstrates and validates a method that combines the two modelling frameworks to exploit their respective strengths: Emission and propagation based prediction of traffic noise, the predominant source of noise at the level of streetscapes, and a LUR-based correction for noise sources that vary on spatial scales beyond the streetscape.Multi-criteria analysis, location-allocation modelling and stakeholder consultation identified 220 monitoring sites with optimal coverage for a 1-week sampling period. A subset of sites was used to validate a road traffic noise emission and propagation model and to specify a LUR model that predicted the contribution of other sources. The equivalent 24-h sound pressure level (LAeq) for all sites was 62.9 dBA (SD 6.4). This varied by time of day, weekday, types of roads and land uses. The traffic noise emission model demonstrated a high level of covariance with observed noise levels, with R² values of 0.58, 0.60 and 0.59 for daytime, nighttime and 24-h periods, respectively. Combined with LUR models to correct for other noise sources, the hybrid models R² values were 0.64, 0.71 and 0.67 for the respective time periods.The study showed that road traffic noise emissions account for most of the variability of total environmental noise in Toronto. The combined approach to predict fine resolution noise exposures with emission and receptor-based models presents an effective alternative to noise modelling approaches based on emission and propagation or LUR modelling.

Emissions from the transportation sector are rapidly changing worldwide; however, the interplay of such emission changes in the face of climate change are not as well understood. This two-part study examines the impact of projected emissions from the U.S. transportation sector (Part I) on ambient air quality in the face of climate change (Part II). In Part I of this study, we describe the methodology and results of a novel Technology Driver Model (see graphical abstract) that includes 1) transportation emission projections (including on-road vehicles, non-road engines, aircraft, rail, and ship) derived from a dynamic technology model that accounts for various technology and policy options under an IPCC emission scenario, and 2) the configuration/evaluation of a dynamically downscaled Weather Research and Forecasting/Community Multiscale Air Quality modeling system.By 2046–2050, the annual domain-average transportation emissions of carbon monoxide (CO), nitrogen oxides (NOx), volatile organic compounds (VOCs), ammonia (NH3), and sulfur dioxide (SO2) are projected to decrease over the continental U.S. The decreases in gaseous emissions are mainly due to reduced emissions from on-road vehicles and non-road engines, which exhibit spatial and seasonal variations across the U.S. Although particulate matter (PM) emissions widely decrease, some areas in the U.S. experience relatively large increases due to increases in ship emissions. The on-road vehicle emissions dominate the emission changes for CO, NOx, VOC, and NH3, while emissions from both the on-road and non-road modes have strong contributions to PM and SO2 emission changes. The evaluation of the baseline 2005 WRF simulation indicates that annual biases are close to or within the acceptable criteria for meteorological performance in the literature, and there is an overall good agreement in the 2005 CMAQ simulations of chemical variables against both surface and satellite observations.