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.

The largest and most variable step of selenium (Se) assimilation into aquatic ecosystems is the rapid uptake of aqueous Se by primary producers. These organisms can transfer more harmful forms of Se to higher trophic levels via dietary pathways, although much uncertainty remains around this step of Se assimilation due to site-specific differences in water chemistry, hydrological and biogeochemical characteristics, and community composition. Thus, predictions of Se accumulation are difficult, and boreal lake systems are relatively understudied. To address these knowledge gaps, five static-renewal field experiments were performed to examine the bioaccumulation of low, environmentally relevant concentrations of Se, as selenite, by naturally grown periphyton from multiple boreal lakes. Periphyton rapidly accumulated Se at low aqueous Se concentrations, with tissue Se concentrations ranging from 8.0 to 24.9 μg/g dry mass (dm) in the 1–2 μg Se/L treatments. Enrichment functions ranged from 2870 to 12 536 L/kg dm in the 4 μg Se/L treatment, to 11 867–22 653 L/kg dm in the 0.5 μg Se/L treatment among lakes. Periphyton Se uptake differed among the five study lakes, with periphyton from mesotrophic lakes generally accumulating more Se than periphyton from oligotrophic lakes. Higher proportions of charophytes and greater dissolved inorganic carbon in more oligotrophic lakes corresponded to less periphyton Se uptake. Conversely, increased proportions of bacillariophytes and total dissolved phosphorus in more mesotrophic lakes corresponded to greater periphyton Se uptake. Periphyton community composition and water chemistry variables were correlated, limiting interpretation of differences in periphyton Se accumulation among lakes. The results of this research provide insight on the biodynamics of Se assimilation at the base of boreal lake food webs at environmentally relevant concentrations, which can potentially inform ecological risk assessments in boreal lake ecosystems in North America.

Effects of commercial sand mining on aquatic diversity are of increasing global concern, especially in parts of some developing countries. However, understanding of this activity on the diversity of macroinvertebrates remains focused on the α component of species diversity, rather than community functioning. Thus, there remains much uncertainty regarding how each component of taxonomic (TD) and functional (FD) diversity respond to the activity both in freshwater and marine environments. Here, we assessed the effect of sand dredging on α, β and γ components of TD and FD during different dredging periods based on the response of macroinvertebrate communities over 4 years in the second largest freshwater lake in China. After three years of active dredging, substantial reductions in each component (α, β and γ) of TD and FD were observed within the dredged area. Moreover, after one year of natural recovery, a distinct restoration was observed with an obvious return in multiple facets of TD and FD indices. No such changes were observed within the adjacent and reference areas. Decreases in the multiple components of TD and FD within the dredged area were most likely associated with the direct extraction of substrate and associated benthic fauna and indirect variations of the water and sediment environment (e.g., increases in water depth and decreases in %Clay). Furthermore, dispersal processes and mass effects mainly contributed to the maintenance of TD and FD during the dredged and recovery stages. In addition, the fast recovery of TD and FD was also related to the simple taxonomic structure and highly connected nature of the study area. Our results suggest that a more precise experimental design (BACI) should be pursued to avoid potentially confounding effects (e.g., natural disturbance) because the sensitivity of diversity indices depends upon different experimental designs. Moreover, measurement of the impacts of sand dredging on macroinvertebrate diversity can be undertaken within a rigorous framework for better understanding the patterns and processes of each component of TD and FD under the sand dredging disturbance.

Organic fraction municipal solid waste (OFMSW) has a high potential for energy and value-added product recovery due to its carbon- and nutrient-rich composition; however, traditional value chains have treated OFMSW as an undesired by-product. This study focuses on value chain optimisation to assist the transition to resource recovery value chains. To achieve this, this work combined two stage stochastic mathematical optimisation with geographical spatial analysis and time series waste generation analysis. Existing infrastructure in England, including anaerobic digestion plants and road transportation networks, were included in the model. To account for uncertainty in waste generation, multiple scenarios and their associated probabilities were developed based on environmental variables. The optimisation problem was solved to further advance the understanding of economically optimal waste-to-resource value chains under waste generation variability. The pertinent decision variables included sizing, technology selection, waste flows and location of thermochemical treatment sites. The model highlights the potential reduction in system profitability as a result of different operating constraints, such as minimum plant operating capacity factors and landfill taxation. The latter was shown to have the largest impact on profitability as overconservative systems designs were implemented to hedge against the waste variability. Such computer-aided models offer opportunities to overcome the challenges posed by waste generation variability and waste to resource value chain transformation.

The assessment of premature mortality associated with the dramatic changes in fine particulate matter (PM₂.₅) and ozone (O₃) has important scientific significance and provides valuable information for future emission control strategies. Exposure data are particularly vital but may cause great uncertainty in health burden assessments. This study, for the first time, used six methods to generate the concentration data of PM₂.₅ and O₃ in China between 2014 and 2018, and then quantified the changes in premature mortality due to PM₂.₅ and O₃ using the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) model. The results show that PM₂.₅-related premature mortality in China decreases by 263 (95% confidence interval (CI95): 142–159) to 308 (CI95: 213–241) thousands from 2014 to 2018 by using different concentration data, while O₃-related premature mortality increases by 67 (CI95: 26–104) to 103 (CI95: 40–163) thousands. The estimated mean changes are up to 40% different for the PM₂.₅-related mortality, and up to 30% for the O₃-related mortality if different exposure data are chosen. The most significant difference due to the exposure data is found in the areas with a population density of around 10³ people/km², mostly located in Central China, for both PM₂.₅ and O₃. Our results demonstrate that the exposure data source significantly affects mortality estimations and should thus be carefully considered in health burden assessments.

Gaining insight into the response of surface ozone (O₃) formation to its precursors plays an important role in the policy-making of O₃ pollution control. However, the real atmosphere is an open and dissipative system, and its complexity poses a great challenge to the study of nonlinear relations between O₃ and its precursors. At present, model-based methods based on reductionism try to restore the real atmospheric photochemical system, by coupling meteorological model and chemical transport model in temporal and spatial resolution completely. Nevertheless, large inconsistencies between predictions and true values still exist, due to the great uncertainty originated from emission inventory, photochemical reaction mechanism and meteorological factors. Recently, based on field observations, some nonlinear methods have successfully revealed the complex emergent properties (long-term persistence, multi-fractal, etc) in coupling correlation between O₃ and its precursors at different time scales. The emergent properties are closely associated with the intrinsic dynamics of atmospheric photochemical system. Taking them into account when building O₃ prediction model, is helpful to reduce the uncertainty in the results. Nonlinear methods (fractal, chaos, etc) based on holism can give new insights into the nonlinear relations between O₃ and its precursors. Changes of thinking models in methodology are expected to improve the precision of forecasting O₃ concentration. This paper has reviewed the advances of different methods for studying the sensitivity of O₃ formation to its precursors during the past few decades. This review highlights that it is necessary to incorporate the emergent properties obtained by nonlinear methods into the modern models, for assessing O₃ formation under combined air pollution environment more accurately. Moreover, the scaling property of coupling correlation detected in the real observations of O₃ and its precursors could be used to test and improve the simulation performance of modern models.

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.

Groundwater contamination originating from anthropogenic industrial activities is a global concern, adversely impacting health of living organisms and affecting natural ecosystems. Monitoring contamination in a complex groundwater system is often limited by sparse data and poor hydrogeological delineation, so that numerous indicators (organic, inorganic, isotopic) are frequently used simultaneously to reduce uncertainty. We suggest that selected Technology-Critical Elements (TCEs), which are usually found in very low concentrations in the groundwater environment, might serve as contamination indicators that can be monitored through aquifer systems. Here, we demonstrate the use of selected TCEs (in particular, Y, Rh, Tl, Ga, and Ge) as indicators for monitoring anthropogenic groundwater contamination in two different groundwater systems, near the Dead Sea, Israel. Using these TCEs, we show that the sources of local groundwater contamination are phosphogypsum ponds located adjacent to fertilizer plants in two industrial areas. In addition, we monitored the spatial distribution of the contaminant plume to determine the extent of well and spring contamination in the region. Results show significant contamination of the groundwater beneath both fertilizer plants, leading to contamination of a series of wells and two natural springs. The water in these springs contains elevated concentrations of toxic metals; U and Tl levels, among others, are above the maximum concentration limits for drinking water.

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.

There must be some uncertainty in the remediation areas delineated based on limited sample points, and resampling in the high-uncertainty areas is particularly necessary. In situ field portable X-ray fluorescence spectrometry (FPXRF), a rapid and cheap analysis method for soil heavy metals, is strongly affected by many spatially non-stationary soil factors. This study first delineated the high-uncertainty area (threshold-exceeding probabilities (PTE) between 30% and 70%) of soil Pb based on the 1000 realizations produced by sequential Gaussian simulation (SGS) with 93 ICP-MS Pb concentrations measured in a peri-urban agriculture area, China. Next, in situ FPXRF was used to increase sample density in this high-uncertainty area. Then, robust geographically weighted regression (RGWR) was used to correct the in situ FPXRF Pb, and the correction accuracies of RGWR, basic GWR, and traditionally-used ordinary least squares regression (OLSR) were compared. Finally, to explore the best way to combine these corrected in situ FPXRF concentrations in delineating the remediation area, we compared the following spatial simulation methods: basic SGS, sequential Gaussian co-simulation (CoSGS) with the RGWR-corrected in situ FPXRF Pb as auxiliary soft data (CoSGS-CorFPXRF), and SGS with the RGWR-corrected in situ FPXRF Pb as part of hard data (SGS-CorFPXRF). Results showed that (i) RGWR produced higher correction accuracy (RI = 71.5%) than GWR (RI = 59.68%) and OLSR (RI = 25.58%) for the in situ FPXRF Pb; (ii) SGS-CorFPXRF produced less uncertainty (G = 0.97) than CoSGS-CorFPXRF (G = 0.95) and SGS (G = 0.91) in the spatial simulation; (iii) High-uncertainty area (30%<PTE<70%) was reduced from 36.55% to 8.7% of the whole study area. It is concluded that the recommended methods are cost-effective to reduce the uncertainty in delineating the remediation areas of soil heavy metals.