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Integration of α, β and γ components of macroinvertebrate taxonomic and functional diversity to measure of impacts of commercial sand dredging
2021
Meng, Xingliang | Cooper, Keith M. | Liu, Zhenyuan | Li, Zhengfei | Chen, Juanjuan | Jiang, Xuankong | Ge, Yihao | Xie, Zhicai
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.
显示更多 [+] 显示较少 [-]Stochastic optimisation of organic waste-to-resource value chain
2021
Robles, Ivan | Durkin, Alex | Guo, Miao
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.
显示更多 [+] 显示较少 [-]Effects of using different exposure data to estimate changes in premature mortality attributable to PM2.5 and O3 in China
2021
Wang, Chunlu | Wang, Yiyi | Shi, Zhihao | Sun, Jinjin | Gong, Kangjia | Li, Jingyi | Qin, Momei | Wei, Jing | Li, Tiantian | Kan, Haidong | Hu, Jianlin
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.
显示更多 [+] 显示较少 [-]A review on methodology in O3-NOx-VOC sensitivity study
2021
Liu, Chunqiong | Shi, Kai
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.
显示更多 [+] 显示较少 [-]Oxidation and sources of atmospheric NOx during winter in Beijing based on δ18O-δ15N space of particulate nitrate
2021
Zhang, Zhongyi | Guan, Hui | Xiao, Hongwei | Liang, Yue | Zheng, Nengjian | Luo, Li | Liu, Cheng | Fang, Xiaozhen | Xiao, Huayun
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.
显示更多 [+] 显示较少 [-]Selected technology-critical elements as indicators of anthropogenic groundwater contamination
2021
Amiel, Nitai | Dror, Ishai | Zurieli, Arik | Livshitz, Yakov | Reshef, Guy | Berkowitz, Brian
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.
显示更多 [+] 显示较少 [-]Tree manipulation experiment for the short-term effect of tree cutting on N2O emission: A evaluation using Bayesian hierarchical modeling
2021
Nishina, Kazuya | Takenaka, Chisato | Ishizuka, Shigehiro | Hashimoto, Shōji
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.
显示更多 [+] 显示较少 [-]Resampling with in situ field portable X-ray fluorescence spectrometry (FPXRF) to reduce the uncertainty in delineating the remediation area of soil heavy metals
2021
Qu, Mingkai | Chen, Jian | Huang, Biao | Zhao, Yongcun
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.
显示更多 [+] 显示较少 [-]Long-term air pollution and other risk factors associated with COVID-19 at the census tract level in Colorado
2021
Berg, Kevin | Romer Present, Paul | Richardson, Kristy
Previous nationwide studies have reported links between long-term concentrations of fine particulate matter (PM2.5) and COVID-19 infection and mortality rates. In order to translate these results to the state level, we use Bayesian hierarchical models to explore potential links between long-term PM2.5 concentrations and census tract-level rates of COVID-19 outcomes (infections, hospitalizations, and deaths) in Colorado. We explicitly consider how the uncertainty in PM2.5 estimates affects our results by comparing four different PM2.5 surfaces from academic and governmental organizations. After controlling for 20 census tract-level covariates, we find that our results depend heavily on the choice of PM2.5 surface. Using PM2.5 estimates from the United States EPA, we find that a 1 μg/m³ increase in long-term PM2.5 concentrations is associated with a statistically significant 26% increase in the relative risk of hospitalizations and a 34% increase in mortality. Results for all other surfaces and outcomes were not statistically significant. At the same time, we find a clear association between communities of color and COVID-19 outcomes at the Colorado census tract level that is minimally affected by the choice of PM2.5 surface. A per-interquartile range (IQR) increase in the percent of non-African American people of color was associated with a 31%, 43%, and 56% increase in the relative risk of infection, hospitalization, and mortality respectively, while a per-IQR increase in the proportion of non-Hispanic African Americans was associated with a 4% and 7% increase in the relative risk of infections and hospitalizations. The current disagreement among the different PM2.5 estimates is a key factor limiting our ability to link environmental exposures and health outcomes at the census tract level. These results have strong implications for the implementation of an equitable public health response during the crisis and suggest targeted areas for additional air monitoring in Colorado.
显示更多 [+] 显示较少 [-]Assessing the regional biogenic methanol emission from spring wheat during the growing season: A Canadian case study
2021
Cai, Mengfan | An, Chunjiang | Guy, Christophe | Lü, Chen | Mafakheri, Fereshteh
As a volatile organic compound existing in the atmosphere, methanol plays a key role in atmospheric chemistry due to its comparatively high abundance and long lifetime. Croplands are a significant source of biogenic methanol, but there is a lack of systematic assessment for the production and emission of methanol from crops in various phases. In this study, methanol emissions from spring wheat during the growing period were estimated using a developed emission model. The temporal and spatial variations of methanol emissions of spring wheat in a Canadian province were investigated. The averaged methanol emission of spring wheat is found to be 37.94 ± 7.5 μg·m⁻²·h⁻¹, increasing from north to south and exhibiting phenological peak to valley characteristics. Moreover, cold crop districts are projected to be with higher increase in air temperature and consequent methanol emissions during 2020–2099. Furthermore, the seasonality of methanol emissions is found to be positively correlated to concentrations of CO, filterable particulate matter, and PM₁₀ but negatively related to NO₂ and O₃. The uncertainty and sensitivity analysis results suggest that methanol emissions show a Gamma probabilistic distribution, and growth length, air temperature, solar radiation and leafage are the most important influencing variables. In most cases, methanol emissions increase with air temperature in the range of 3–35 °C while the excessive temperature may result in decreased methanol emissions because of inactivated enzyme activity or increased instant methanol emissions due to heat injury. Notably, induced emission might be the major source of biogenic methanol of mature leaves. The results of this study can be used to develop appropriate strategies for regional emission management of cropping systems.
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