Tropospheric Ozone Perturbations Induced by Urban Land Expansion in China from 1980 to 2017.

Urbanization perturbs air pollutants from a dynamic and thermodynamic perspective, which has inspired extensive investigations in China due to rapid urban land expansion in the past four decades. However, knowledge gaps remain in the long-term and nationwide responses to air pollutants to urbanization. The evolution of tropospheric ozone associated with urban land expansion across China was assessed from 1980 to 2017 using a coupled WRF-Chem model based on a recently updated land use change (LUC) data set. The results revealed that urban-land expansion drove growing ozone trends for this period and contributed about 3-9% to its summer maximum concentrations during the 2010s in major urban agglomerations across China. The association between a long-term change in summer O3 concentrations and LUC after excluding the effect of precursor emissions and meteorological conditions and causes of interannual (short-term) variations in O3 concentrations induced by urban-land expansion were also explored by examining the relationships between ozone fluctuations and meteorological variables.

Drivers of Global Methane Emissions Embodied in International Beef Trade.

Increasing worldwide demand for beef products promotes international beef trade. Cattle raising and beef products as significant sources of methane (CH4) emissions have received widespread concerns. However, the factors driving CH4 emissions embodied in the global beef trade have not been quantified. Here, we evaluate international beef trade-induced CH4 emissions and assess the contribution of the five driving factors to changes in CH4 emissions embodied in the beef trade from 2000 to 2018. We show that driven by increasing population and meat demands, the global beef trade-induced CH4 emissions increased continuously in the past two decades, with total emissions of 9337.3 Gg in 2018. The drivers that could potentially reduce trade-related emissions are emission intensities in beef exporting countries and beef importing countries' selections of their beef suppliers. Together, these two driving factors reduced CH4 emissions by 923.5 Gg from 2012 to 2018. Results suggest that efforts should be made to reduce the emission intensity via improving cattle feed and feeding practices in beef exporting countries. Beef importing countries could also contribute to CH4 emission reduction by selecting those beef exporting countries with low emission intensities.

Simulation of the impact of the emergency control measures on the reduction of air pollutants: a case study of APEC blue.

Serious air pollution motivates governments to take control measures. However, specific emission reduction effects of various temporary emission reduction policies are difficult to evaluate. During the Asia-Pacific Economic Cooperation meeting in Beijing in 2014, the Chinese government implemented a number of emergency emission control measures in the Beijing-Tianjin-Hebei area to maintain the air quality in this region. This gave us an opportunity to quantify the effectiveness of the emission reduction measures separately and identify the efficient policy combinations for the reduction of major pollutants. In this study, we evaluated the impacts of specific emission reduction measures on the concentrations of two major air pollutants (PM2.5 and O3) under eight policy scenarios using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). Comparing these scenarios, we found that the control policies against the primary PM2.5 emission achieved the most significant results. Meanwhile, all the emission control measures raised the ozone concentrations in different degrees, which might be partly attributed to the changes of PM2.5 concentration and the ratio of NOx and VOCs caused by the emission control measures. Our results suggest that, in VOC-sensitive areas like Beijing, emergency control measures focusing on primary PM2.5 emission could lead to significant PM2.5 reduction and relatively small ozone increase, and should be considered as a priority policy. Joint emission control at the regional scale is also important especially under unfavorable meteorological conditions.

Identification of two-phase flow patterns based on capacitance data of electrical capacitance tomography with semi-supervised generative adversarial network.

Currently, the flow pattern identification algorithms based on ECT (electrical capacitance tomography) technology have low identification accuracy for complex flow patterns and require a large amount of label data for learning. A novel flow pattern identification method based on a semi-supervised generative adversarial network (SGAN) with capacitance data of ECT is proposed. First, the principles of the ECT technique and general GAN are briefly described, and the model parameters, loss function, and training process of the SGAN are explained in detail. Second, a capacitance data sample set of 11 400 random flow patterns is constructed by co-simulations of COMSOL and MATLAB, and then, the SGAN and BP (back propagation) and SVM (support vector machine) network models are trained and validated by the training set. Finally, static experiments are conducted on the self-developed ECT system, and the identification results of different algorithms are compared and analyzed by modifying the label sample size of the training set. The experimental results show that SGAN maintains a higher average identification accuracy under the training condition where the number of label samples of SGAN is ten times smaller than that of the other two algorithms.

The carbon footprint response to projected base stations of China's 5G mobile network.

While the rapid expansion of China's 5G mobile network helps to speed up the nation's economic and social development, it tends to release more CO2 due to the 5G's significant energy demand, hampering sustainable development of the 5G network. Previous assessments of CO2 emissions from China's 5G development were based on a projected 5G network ranging from six to fifteen million base stations with the absent of a convincing business model in 5G's application. Under the scenario of business-estimated six million base stations in 2030, the share of electricity consumed by China's 5G networks in 2030 could reach 8.4 % of the national total power generation, causing 0.44 GtCO2/yr CO2 emissions. We collected 5G base station numbers in 2020 and 2021 in 31 provinces and province-level municipalities (PLM), the period with the rapid growth of the 5G base stations in China. We linked these provincial base stations with provincial Gross Domestic Product (GDP), population (POP), and big data development level (BDDL) and established a statistical model to predict 5G base stations by 2030. The model predicted 2-5 million 5G base stations by 2030, considerably lower than the business-projected base station number. Under the model predicted 5G base stations, China's 5G network could yield 0.15-0.29 GtCO2/yr emissions subject to the nation's BDDL from 40 to 80 % by 2030. Both 5G base stations and CO2 emissions are significantly lower than the previous estimates. We decomposed the CO2 footprint of China's 5G networks and assessed the contribution of the number of 5G base stations and mobile data traffic to 5G-induced CO2 emissions. We find that increasing the application of clean energy and promoting energy efficiency can reduce CO2 emissions in the 5G network. To more accurately estimate 5G's climate effect, we propose that it urgently needs to improve vivid 5G business models.

Mesoscale cycling of organophosphorus flame retardants (OPFRs) in the Bohai Sea and Yellow Sea biotic and abiotic environment: A WRF-CMAQ modeling.

Rapid urbanization and industrialization in the eastern seaboard region of China enhance the widespread use of organophosphorus flame retardants (OPFRs). The present study set up a coupled WRF-CMAQ-SMOKE and multi-compartment exchange modeling framework to assess the environmental fate and cycling of OPFRs and their contamination in the Bohai and Yellow Seas' marine food web. The framework predicts meteorological conditions, optimized air emissions, and concentrations of OPFRs in air, seawaters, marine sediment, and the food web. The model was implemented to simulate the temporal and spatial fluctuations of Tris (2-chloroisopropyl) phosphate (TCPP), the most dominant congener of OPFRs in China, in the Bohai and the Yellow Sea ecosystems on a spatial resolution of 10 km. Results revealed the effects of source proximity, atmospheric transport and deposition, and the changes in meteorology on TCPP's temporal-spatial distribution across different areas of coastal waters. The model also captures TCPP levels in commercial fish species in the Bohai Sea. The detailed temporal-spatial characteristics of TCPP with the mesoscale resolution provide useful information and a new tool for the environmental and health consequences of mariculture, urban and industrial emission mitigation in coastal regions for emerging chemicals, and fishery industry development.

The footprint of dioxins in globally traded pork meat.

The bioaccumulation of polychlorinated dibenzo-p-dioxins/furans (PCDD/Fs), known as dioxins, in fatty meat is one of primary pathways of entry into the human body, but levels of human exposure to dioxins in fatty meat subject to global trade are unknown. We show high dioxin estimated dietary intake (EDI) via pork consumption in Europe, the United States, and China, owing to stronger dioxin environmental contamination and high pork consumption in these countries. The dioxin risk transfer embodied in pork trade is mostly significant in high-latitude countries and regions of Canada, Russia, and Greenland because these regions with low dioxin environmental levels import large amounts of pork meat from more severely dioxin-contaminated Europe and the United States. We demonstrate that global pig feed trading decreases the exposure of pork consumers to dioxins via the import of feed from countries with low dioxin environmental contamination by pig breeding countries.