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Urban sprawl has become the main pattern of spatial expansion in many large cities in China, and its ecological and environmental effects profoundly impact Chinese urban development. In this paper, nighttime light data and statistical yearbook data are adopted as basic data sources to simulate the evolution trend of the urban sprawl in the upper Yangtze River (UYR), China. First, the urban sprawl index (USI) is employed to assess the level of urban sprawl and to determine the characteristics of urban sprawl under different scales. Second, the spatial autocorrelation model is applied to reveal the spatial pattern change characteristics of urban sprawl from 1992 to 2015. Third, a scenario analysis model of urban sprawl is constructed to simulate the evolution trend of the urban sprawl under different scenarios. Finally, based on the Geodetector, the influence of factors and factor interactions influencing urban sprawl in different time periods is analyzed. The results yield the following main conclusions: (1) The urban sprawl in the UYR first intensifies and then stabilizes over time. The number of cities with high USI in Sichuan province, medium cities, and Chengdu-Chongqing urban agglomeration increases over time, indicating that urban sprawl is intensifying in these areas. (2) The urban sprawl hot spots experience a pattern transformation process of point-like expansion-point-ring expansion-point-axis expansion-axis radiation. (3) Under the scenarios with different scales, the urban land sprawl in large cities is the highest, accounting for more than 47% of the UYR. Urban land sprawl extent in the Chengdu-Chongqing urban agglomeration is the highest, accounting for more than 51% of the UYR. The cities exhibiting the highest sprawl are Chongqing, Lijiang, and Kunming, accounting for 25.84%, 7.37%, and 5.11%, respectively, of the UYR. (4) In the different time scenario simulations, the urban land in large cities exhibits the highest sprawl, accounting for approximately 48.16% of the UYR. The urban land in the Chengdu-Chongqing urban agglomeration demonstrates the highest sprawl, accounting for 50.92% of the UYR. (5) From 1996 to 2002, the driver with the highest influence on urban sprawl was secondary industry share of GDP, with a q -statistic of 0.616. From 2009 to 2015, the driver with the highest influence on urban sprawl was green space per capita with a q -statistic of 0.396.

Soil contamination in urban environment by trace metals is of public concerns. For better risk assessment, it is important to determine their background concentrations in urban soils. For this study, we determined the background concentrations of 9 trace metals including As, Ba, Cd, Co, Cu, Ni, Pb, Se, and Zn in 214 urban soils in Florida from two large cities (Orlando and Tampa) and 4 small cities (Clay County, Ocala, Pensacola and West Palm Beach). The objectives were to determine: 1) total concentrations of trace metals in urban soils in cities of different size; 2) compare background concentrations to Florida Soil Cleanup Target Levels (FSCTLs); and 3) determine their distribution and variability in urban soils via multivariate statistical analysis. Elemental concentrations in urban soils were variable, with Pb being the highest in 5 cities (165-552 mg kg -1 ) and Zn being the highest concentration in Tampa (1,000 mg kg -1 ). Besides, the As and Pb concentrations in some soils exceeded the FSCTL for residential sites at 2.1 mg kg -1 As and 400 mg kg -1 Pb. Among the cities, Clay County and Orlando had the lowest concentrations for most elements, with Cd, Co, and As being the lowest while Ba, Pb and Zn being the highest. Among all values, geometric means were the lowest while 95th percentile was the highest for all metals. Most 95th percentile values were 2-3 folds higher than the GM data, with Pb presenting the greatest difference, being 4 times greater than GM value (58.9 vs. 13.6 mg kg -1 ). Still they were lower than FSCTL, with As exceeding FSCTL for residential sites at 2.1 mg kg -1 . In addition, the linear discriminate analysis showed distinct separation among the cities: Ocala (Ba & Ni) and Pensacola (As & Pb) were distinctly different from each other and from other cities with higher metal concentrations. The large variations among elemental concentrations showed the importance to establish proper background concentrations of trace metals in urban soils., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Published by Elsevier Ltd.)

Carbon accounting results for the same city can differ due to differences in protocols, methods, and data sources. A critical review of these differences and the connection among them can help to bridge our knowledge between university-based researchers and protocol practitioners in accounting and taking further mitigation actions. The purpose of this study is to provide a review of published research and protocols related to city carbon accounting, paying attention to both their science and practical actions. To begin with, the most cited articles in this field are identified and analyzed by employing a citation network analysis to illustrate the development of city-level carbon accounting from three perspectives. We also reveal the relationship between research methods and accounting protocols. Furthermore, a timeline of relevant organizations, protocols, and projects is provided to demonstrate the applications of city carbon accounting in practice. The citation networks indicate that the field is dominated by pure-geographic production-based and community infrastructure-based accounting; however, emerging models that combine economic system analysis from a consumption-based perspective are leading to new trends in the field. The emissions accounted for by various research methods consist essentially of the scope 1-3, as defined in accounting protocols. The latest accounting protocols include consumption-based accounting, but most cities still limit their accounting and reporting from pure-geographic production-based and community infrastructure-based perspectives. In conclusion, we argue that protocol practitioners require support in conducting carbon accounting, so as to explore the potential in mitigation and adaptation from a number of perspectives. This should also be a priority for future studies.

Although PM 2.5 (particulate matter with aerodynamic diameters less than 2.5 μm) in the air originates from emissions, its concentrations are often affected by confounding meteorological effects. Therefore, direct comparisons of PM 2.5 concentrations made across two periods, which are commonly used by environmental protection administrations to measure the effectiveness of mitigation efforts, can be misleading. Here, we developed a two-step method to distinguish the significance of emissions and meteorological factors and assess the effectiveness of emission mitigation efforts. We modeled ambient PM 2.5 concentrations from 1980 to 2014 based on three conditional scenarios: realistic conditions, fixed emissions, and fixed meteorology. The differences found between the model outputs were analyzed to quantify the relative contributions of emissions and meteorological factors. Emission-related gridded PM 2.5 concentrations excluding the meteorological effects were predicted using multivariate regression models, whereas meteorological confounding effects on PM 2.5 fluctuations were characterized by probabilistic functions. When the regression models and probabilistic functions were combined, fluctuations in the PM 2.5 concentrations induced by emissions and meteorological factors were quantified for all model grid cells and regions. The method was then applied to assess the historical and future trends of PM 2.5 concentrations and potential fluctuations on global, national, and city scales. The proposed method may thus be used to assess the effectiveness of mitigation actions.

As national efforts to reduce CO 2 emissions intensify, policy-makers need increasingly specific, subnational information about the sources of CO 2 and the potential reductions and economic implications of different possible policies. This is particularly true in China, a large and economically diverse country that has rapidly industrialized and urbanized and that has pledged under the Paris Agreement that its emissions will peak by 2030. We present new, city-level estimates of CO 2 emissions for 182 Chinese cities, decomposed into 17 different fossil fuels, 46 socioeconomic sectors, and 7 industrial processes. We find that more affluent cities have systematically lower emissions per unit of gross domestic product (GDP), supported by imports from less affluent, industrial cities located nearby. In turn, clusters of industrial cities are supported by nearby centers of coal or oil extraction. Whereas policies directly targeting manufacturing and electric power infrastructure would drastically undermine the GDP of industrial cities, consumption-based policies might allow emission reductions to be subsidized by those with greater ability to pay. In particular, sector-based analysis of each city suggests that technological improvements could be a practical and effective means of reducing emissions while maintaining growth and the current economic structure and energy system. We explore city-level emission reductions under three scenarios of technological progress to show that substantial reductions (up to 31%) are possible by updating a disproportionately small fraction of existing infrastructure.