The health effects of potentially toxic elements (PTEs) in airborne particulate matter (PM) are strongly dependent on their size distribution and dissolution. This study examined PTEs within nine distinct sizes of PM in a Chinese megacity, with a focus on their deposited and dissolved bioaccessibility in the human pulmonary region. A Multiple Path Particle Dosimetry (MPPD) model was used to estimate the deposited bioaccessibility, and an in-vitro experiment with simulated lung fluid was conducted for dissolved bioaccessibility. During the non-heating season, the dissolved bioaccessible fraction (DBF) of As, Cd, Co, Cr, Mn, Pb and V were greater in fine PM (aerodynamics less than 2.1 μm) than in coarse PM (aerodynamics between 2.1 and 10 μm), and vice versa for Ni. With the increased demand of heating, the DBF of Pb and As decreased in fine particle sizes, probably due to the presence of oxide/silicate compounds from coal combustion. Inhalation health risks based on the bioaccessible concentrations of PTEs displayed the peaks in <0.43 μm and 2.1–3.3 μm particulate sizes. The non-cancer risk was at an acceptable level (95th percentiles of hazard index (HI) was 0.49), but the cancer risk exceeded the threshold value (95th percentiles of total incremental lifetime cancer risk (TCR) was 8.91 × 10⁻⁵). Based on the results of uncertainty analysis, except for the exposure frequency, the total concentrations and DBF of As and Cr in <0.43 μm particle size segment have a greater influence on the uncertainty of probabilistic risk.

In this study, an interval two-stage fuzzy fractional programming (TFFP) method is developed to facilitate collaborative governance of economy and water resources. Methods of interval programming, fuzzy programming, two-stage programming, and fractional programming are integrated within a general system optimization framework. The main contribution of TFFP is simultaneously addressing various uncertainties and tackling trade-offs between environmental and economic objectives in the optimized schemes for water resources allocation. A case study of a highly urbanized coastal city (i.e., Shenzhen) in China is provided as an example for demonstrating the proposed approach. According to the results, industrial sectors should receive 34.8% of total water supply, while agricultural sectors should receive 1.5%. For the spatial allocation of water resources, Bao An, Long Gang, and Fu Tian districts should be allocated 21.6%, 20.5%, and 14.8% water to promote the economic development. The discharge analysis indicates that chemical oxygen demand (CODcᵣ) and total phosphorus (TP) would be key pollutants. Moreover, the optimized seawater desalination volume would be negligibly influenced by price, while the upper bounds of desalination would be increased with the raising acceptable credibility levels in the period of 2031–2035. Analysis of desalination prices also reveals that the decision-makers should increase the scale of desalination in the period of 2021–2025. In addition, the effectiveness and applicability of TFFP would be evaluated under economic maximization scenarios. The result showed that the economic maximization scenario could obtain higher economic benefits, but it would be accompanied by a larger number of pollutant discharges. It is expected that this study will provide solid bases for planning water resources management systems in coastal regions.

Plume chasing is cost-effective, measuring individual, on-road vehicular emissions. Whereas, wake-flow-generated turbulence results in intermittent, rapid pollutant dilution and substantial fluctuating concentrations right behind the vehicle being chased. The sampling duration is therefore one of the important factors for acquiring representative (average) concentrations, which, however, has been seldom addressed. This paper, which is based on the detailed spatio-temporal dispersion data after a heavy-duty truck calculated by large-eddy simulation (LES), examines how sampling duration affects the uncertainty of the measured concentrations in plume chasing. The tailpipe dispersion is largely driven by the jet-like flows through the vehicle underbody with approximate Gaussian concentration distribution for x ≤ 0.6h, where x is the distance after the vehicle and h the characteristic vehicle size. Thereafter for x ≥ 0.6h, the major recirculation plays an important role in near-wake pollutant transport whose concentrations are highly fluctuating and positively shewed. Plume chasing for a longer sampling duration is more favourable but is logistically impractical in busy traffic. Sampling duration, also known as averaging time in the statistical analysis, thus has a crucial role in sampling accuracy. With a longer sampling (averaging) duration, the sample mean concentration converges to the population mean, improving the sample reliability. However, this effect is less pronounced in long sampling duration. The sampling accuracy is also influenced by the locations of sampling points. For the region x > 0.6h, the sampling accuracy is degraded to a large extent. As a result, acceptable sample mean is hardly achievable. Finally, frequency analysis unveils the mechanism leading to the variance in concentration measurements which is attributed to sampling duration. Those data with frequency higher than the sampling frequency are filtered out by moving average in the statistical analyses.

Conventional factor analyses can present problems in cases with changing numbers of sources and/or time-dependent source compositions. There is also lack of a reliable method to estimate uncertainties in the source contributions derived by positive matrix factorization (PMF). Applying a moving window evolving PMF to hourly PM₂.₅ composition dataset from a field campaign in Tianjin China that included the Spring and Lantern Festivals and the start of COVID-19 pandemic has substantially improved the apportionment compared to a conventional analysis using the entire data. Festival-related sources (e.g., fireworks and residential burning here) have been clearly identified and estimated during both the Spring and Lantern Festivals. During this period, the sources changed because the time period overlaps with the outbreak of COVID-19 and related reductions in activity during the lockdown that began on Lunar New Year. Multiple PMF runs providing source contribution estimates made it possible to estimate the uncertainties in these values. Our results show that winds-dependent sources like dust and distant point sources have larger uncertainties than the other sources. Compared with conventional PMF analyses, the current method may better reflect the actual emissions as well as being able to estimate uncertainties. Thus, this approach appears to be an improvement if the appropriate data are available.

Water quality monitoring programs often collect large amounts of data with limited attention given to the assessment of the dominant drivers of spatial and temporal water quality variations at the catchment scale. This study uses a multi-model approach: a) to identify the influential catchment characteristics affecting spatial variability in water quality; and b) to predict spatial variability in water quality more reliably and robustly. Tropical catchments in the Great Barrier Reef (GBR) area, Australia, were used as a case study. We developed statistical models using 58 catchment characteristics to predict the spatial variability in water quality in 32 GBR catchments. An exhaustive search method coupled with multi-model inference approaches were used to identify important catchment characteristics and predict the spatial variation in water quality across catchments. Bootstrapping and cross-validation approaches were used to assess the uncertainty in identified important factors and robustness of multi-model structure, respectively. The results indicate that water quality variables were generally most influenced by the natural characteristics of catchments (e.g., soil type and annual rainfall), while anthropogenic characteristics (i.e., land use) also showed significant influence on dissolved nutrient species (e.g., NOX, NH₄ and FRP). The multi-model structures developed in this work were able to predict average event-mean concentration well, with Nash-Sutcliffe coefficient ranging from 0.68 to 0.96. This work provides data-driven evidence for catchment managers, which can help them develop effective water quality management strategies.

The determination of both stable nitrogen (δ¹⁵N–NO₃⁻) and stable oxygen (δ¹⁸O–NO₃⁻) isotopic signatures of nitrate in PM₂.₅ has shown potential for an approach of assessing the sources and oxidation pathways of atmospheric NOx (NO+NO₂). In the present study, daily PM₂.₅ samples were collected in the megacity of Beijing, China during the winter of 2017–2018, and this new approach was used to reveal the origin and oxidation pathways of atmospheric NOx. Specifically, the potential of field δ¹⁵N–NO₃⁻ signatures for determining the NOx oxidation chemistry was explored. Positive correlations between δ¹⁸O–NO₃⁻ and δ¹⁵N–NO₃⁻ were observed (with R² between 0.51 and 0.66, p < 0.01), and the underlying environmental significance was discussed. The results showed that the pathway-specific contributions to NO₃⁻ formation were approximately 45.3% from the OH pathway, 46.5% from N₂O₅ hydrolysis, and 8.2% from the NO₃+HC channel based on the δ¹⁸O-δ¹⁵N space of NO₃⁻. The overall nitrogen isotopic fractionation factor (εN) from NOx to NO₃⁻ on a daily scale, under winter conditions, was approximately +16.1‰±1.8‰ (consistent with previous reports). Two independent approaches were used to simulate the daily and monthly ambient NOx mixtures (δ¹⁵N-NOx), respectively. Results indicated that the monthly mean values of δ¹⁵N-NOx compared well based on the two approaches, with values of −5.5‰ ± 2.6‰, −2.7‰ ± 1.9‰, and −3.2‰ ± 2.2‰ for November, December, and January (2017–2018), respectively. The uncertainty was in the order of 5%, 5‰ and 5.2‰ for the pathway-specific contributions, the εN, and δ¹⁵N-NOx, respectively. Results also indicated that vehicular exhaust was the key contributor to the wintertime atmospheric NOx in Beijing (2017–2018). Our advanced isotopic perspective will support the future assessment of the origin and oxidation of urban atmospheric NOx.

Considerable uncertainty exists with regard to the effects of thinning and harvesting on N₂O emissions as a result of changes caused in the belowground environment by tree cutting. To evaluate on the effects of changes in the belowground environment on N₂O emissions from soils, we conducted a tree manipulation experiment in a Japanese cedar (Cryptomeria japonica) stand without soil compaction or slash falling near measurement chambers and measured N₂O emission at distances of 50 and 150 cm from the tree stem (stump) before and after cutting. In addition, we inferred the effects of logging on the emission using a hierarchical Bayesian (HB) model. Our results showed that tree cutting stimulated N₂O emission from soil and that the increase in N₂O emission depended on the distance from the stem (stump); increase in N₂O emission was greater at 50 than at 150 cm from the stem. Tree cutting caused the estimated N₂O emission at 0–40 cm from the stem to double (the % increase in N₂O emission by tree cutting was 54%–213%, 95% predictive credible interval) when soil temperature was 25 °C and WFPS was 60%. Posterior simulation of the HB model predicted that 30% logging would cause a 57% (47%–67%) increase in N₂O emission at our study site (2000 trees ha⁻¹) considering only the effects of belowground changes by tree cutting during the measurement period.

The identification of nitrate (NO₃⁻) sources and biogeochemical transformations is critical for understanding the different nitrogen (N) pathways, and thus, for controlling diffuse pollution in groundwater affected by livestock and agricultural activities. This study combines chemical data, including environmental isotopes (δ²HH₂O, δ¹⁸OH₂O, δ¹⁵NNO₃, and δ¹⁸ONO₃), with land use/land cover data and a Bayesian isotope mixing model, with the aim of reducing the uncertainty when estimating the contributions of different pollution sources. Sampling was taken from 53 groundwater sites in Comarca Lagunera, northern Mexico, during 2018. The results revealed that the NO₃⁻ (as N) concentration ranged from 0.01 to 109 mg/L, with more than 32% of the sites exceeding the safe limit for drinking water quality established by the World Health Organization (10 mg/L). Moreover, according to the groundwater flow path, different biogeochemical transformations were observed throughout the study area: microbial nitrification was dominant in the groundwater recharge areas with elevated NO₃⁻ concentrations; in the transition zones a mixing of different transformations, such as nitrification, denitrification, and/or volatilization, were identified, associated to moderate NO₃⁻ concentrations; whereas in the discharge area the main process affecting NO₃⁻ concentrations was denitrification, resulting in low NO₃⁻ concentrations. The results of the MixSIAR isotope mixing model revealed that the application of manure from concentrated animal-feeding operations (∼48%) and urban sewage (∼43%) were the primary contributors of NO₃⁻ pollution, whereas synthetic fertilizers (∼5%), soil organic nitrogen (∼4%), and atmospheric deposition played a less important role. Finally, an estimation of an uncertainty index (UI90) of the isotope mixing results indicated that the uncertainties associated with atmospheric deposition and NO₃⁻−fertilizers were the lowest (0.05 and 0.07, respectively), while those associated with manure and sewage were the highest (0.24 and 0.20, respectively).

Twenty-five years after the first look at polycyclic aromatic compounds (PACs) in Canada, this article presents current knowledge on Canadian PAC emission sources. The analysis is based on national inventories (the National Pollutant Release Inventory (NPRI) and the Air Pollutant Emissions Inventory (APEI)), an analysis of Canadian forest fires, and several air quality model-ready emissions inventories. Nationally, forest fires continue to dominate PAC emissions in Canada, however there is uncertainty in these estimates. Though forest fire data show a steady average in the total annual area burned historically, an upward trend has developed recently. Non-industrial sources (home firewood burning, mobile sources) are estimated to be the second largest contributor (∼6-8 times lower than forest fires) and show moderate decreases (25%–65%) in the last decades. Industrial point sources (aluminum production, iron/steel manufacturing) are yet a smaller contributor and have seen considerable reductions (90% +) in recent decades. Fugitive emissions from other industrial sources (e.g. disposals by the non-conventional oil extraction and wastewater sectors, respectively) remain a gap in our understanding of total PAC emissions in Canada. Emerging concerns about previously unrecognized sources such as coal tar-sealed pavement run-off, climate change are discussed elsewhere in this special issue. Results affirm that observations at the annual/national scale are not always reflective of regional/local or finer temporal scales. When determining which sources contribute most to human and ecosystem exposure in various contexts, examination at regional and local scales is needed. There is uncertainty overall in emissions data stemming in part from various accuracy issues, limitations in the scope of the various inventories, and inventory gaps, among others.

Although huge interspecies differences in the response to dioxins have been acknowledged, toxic equivalency factors derived from rodent studies are often used to assess human health risk. To determine interspecies differences, we first developed a toxicokinetic model in humans by measuring dioxin concentrations in environmental and biomonitoring samples from Southern China. Significant positive correlations between dioxin concentrations in blood and age were observed for seven dioxin congeners, indicating an age-dependent elimination rate. Based on toxicokinetic models in humans, the half-lives of 15 dioxin congeners were estimated to be 1.60–28.55 years. In consideration that the highest contribution to total toxic equivalency in blood samples was by 12378-polychlorinated dibenzo-p-dioxin (P₅CDD), this study developed a physiologically based pharmacokinetic (PBPK) model of 12378-P₅CDD levels in the liver, kidney, and fat of C57/6J mice exposed to a single oral dose, and the half-life was estimated to be 26.1 days. Based on estimated half-lives in humans and mice, we determined that the interspecies difference of 12378-P₅CDD was 71, much higher than the default usually used in risk assessment. These results could reduce the uncertainty human risk assessment of 12378-P₅CDD, and our approach could be used to estimate the interspecies differences of other dioxin congeners.