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The public health risk caused by urban floods is a global concern. Flood risks are amplified by the interaction of rainfall and storm tides in coastal cities. In this study, we investigate the flood risks of rainfall and storm tides coupling statistical and hydrodynamic models and evaluate the influence of different parameter estimation methods and bivariate return periods (RPs) on flood risks in the coastal city. The statistical model is used to obtain the bivariate design of rainfall and storm tides with the integration of copula function, most-likely weight function and Monte Carlo simulation method. The bivariate designs are adopted as the input boundaries for the hydrodynamic model established by Personal Computer Storm Water Management Model (PCSWMM), and the flood risk is evaluated by the hydrodynamic model. Subsequently, the influence of different parameter estimation approaches (that is, parametric and non-parametric) and bivariate RPs (that is, co-occurrence RP, joint RP, and Kendall RP) on bivariate designs and flood risks are investigated. With Haikou coastal city in China as the case study, the results show that: (1) Gumbel copula is the best function to describe the correlation structure between rainfall and storm tides for the parametric and non-parametric approaches, and the non-parametric approach is a better fit for the observed data; (2) when the Kendall RP is large (more than 100 years), the flood risk is underestimated with an average of 17% by the non-parametric estimation, and the parametric estimation approach is recommended as it is considered the most unfavorable scenario; (3) the types of bivariate RP have the important impact on the flood risk. When there is no specific application need, the Kendall RP can be adopted as the bivariate design standard of flooding facilities since it can describe the dangerous areas more accurately for multivariate scenario. The results can provide references for reasonable flood risk assessment and flooding facility design in coastal cities., Competing Interests: The authors declare no conflict of interest.

The Central Plains of China, represented by Henan province, faces a dramatic rise in vehicular stock and CO 2 emissions. The refined-resolution(1 km × 1 km) vehicular CO 2 emission inventory for Henan province was developed to identify emission patterns. Results show that CO 2 emissions in Henan province reached 77.04 Mt in 2019, and LDGV and HDDT were the major sources that emitted 42.34% and 35.96% of CO 2 emissions, respectively. Based on gridded emission, Moran's Index was used to identify spatial distribution patterns of vehicular CO 2 . The higher CO 2 emission intensity areas were concentrated in the central and northern of the province and urban areas in each city, especially in Zhengzhou and its surrounding cities. Moreover, the analysis of the driving forces behind the differences in emissions among cities using the multi-regional (M-R) spatial decomposition model revealed that income and population-scale are significant impacts. In cities such as Zhengzhou, emissions may be dramatically increase owing to high economic growth expectations. 'Polarization phenomenon' of CO 2 emission distribution should be vigilant. Findings provided insights for refined policy-making in Henan province to limit CO 2 emission: (1) Take cities as transportation hubs, e.g., Zhengzhou and Shangqiu, and that in the traffic radiation circle, e.g., Jiaozuo and Zhoukou, as the critical areas for CO 2 emission reduction; (2) Promote electric vehicles as replacement for traditional fuel vehicles; especially for cities with large passenger car emissions, such as Zhengzhou, and cities with large truck emissions, such as Shangqiu and Zhoukou; actively guide new consumer groups to choose EVs, especially in cities with high growth expectations such as Zhengzhou; (3) Rely on the advantages of transportation network to promote the 'road to railway' of bulk cargo transportation and mainly focus on highways with higher CO 2 density, such as Beijing-Hong Kong&Macao Expressway, Shanghai-Xi'an Expressway, Da Guang Expressway, and Lian Huo Expressway., 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., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Urban surface water flooding is increasing because of climate change and urbanization, and brings great challenges to urban sustainable development. It is, therefore, most important to develop urban flood management approaches to alleviate the consequences of floods. China is implementing a "sponge city" initiative to tackle urban surface water flooding and improve urban water management. There is, however, limited cost-effectiveness evaluation to support the choice of economically efficient mitigation strategies. To address this gap, this study developed an evaluation framework based on cellular automata and cost-benefit analysis to assess the performance of mitigation strategies in sponge city construction. This approach is demonstrated with a case study of Siergou (Dalian, China), which has a total area of 10.1 km 2 . The mitigation measures of green roofs, permeable pavements and bio-retention were used to generate mitigation scenarios. A two-dimensional cellular automata-based model was used to simulate urban surface water flooding. The results obtained from the case study indicate that the framework can achieve cost-effective mitigation strategies for sponge city construction, which can support robust decision making. The distribution of mitigation strategies has great impacts on the effectiveness of alleviating urban flood risk. This study provides new insight into the development of cost-effective mitigation strategies for sponge city construction., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Achieving carbon neutrality before 2060 newly announced in China are expected to substantially affect air quality. Here we project the pollutants emissions in China based on a carbon neutrality roadmap and clean air policies evolution; national and regional PM 2.5 and O 3 concentrations in 2030 (the target year of carbon peak), 2035 (the target year of "Beautiful China 2035" launched by the Chinese government to fundamentally improve air quality) and 2060 (the target year of carbon neutrality) are then simulated using an air quality model. Results showed that compared with 2019, emissions of SO 2 , NO x , primary PM 2.5 , and VOCs are projected to reduce by 42%, 42%, 44%, and 28% in 2030, by 57%, 58%, 60%, and 42% in 2035, by 93%, 93%, 90% and 61% in 2060 respectively. Consequently, in 2030, 2035, and 2060, the national annual mean PM 2.5 will be 27, 23, and 11 μg m -3 ; and the 90th percentile of daily 8-h maxima of O 3 (O 3 -8h 90th) will be 129, 123, and 93 μg m -3 ; 82%, 94%, and 100% of 337 municipal cities will reach the current national air quality standard, respectively. It's expected that the "Beautiful China 2035" target is very likely to be achieved, and about half of the 337 cities will meet the current WHO air quality guideline in 2060. In the near future, strict environmental policies driven by "Beautiful China 2035" are needed due to their substantial contribution to emission reductions. By 2060, the low-carbon policies driven by the carbon neutrality target are expected to contribute to larger than 80% of reductions in PM 2.5 and O 3 -8h 90th concentrations relative to the 2020 levels, implying that more attention could be paid to low-carbon policies after 2035. Our research would provide implications for future co-governance of air pollution and climate change mitigation in China and other developing countries., 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., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Samples of ultrafine particle matter mass (PM 0.1 ) were collected over 12 months at three cities in California: Los Angeles, East Oakland, San Pablo, and over six months at Fresno. Molecular markers adjusted for volatility and reactivity were used to calculate PM 0.1 source contributions. Wood burning was a significant source of PM 0.1 organic carbon (OC) during the winter months in northern California (17-47%) but made smaller contributions in other months (0-8%) and was minor in all seasons in Los Angeles (0-5%), except December (17%) during holiday celebrations. Meat cooking was the largest source of PM 0.1 OC across all sites (13-29%), followed by gasoline combustion (7-21%). Motor oil and diesel fuel combustion made smaller contributions to PM 0.1 OC (3-10% and 3-7%, respectively). Unresolved sources accounted for 22-56% of the PM 0.1 OC. The lack of a clear seasonal profile for this unresolved OC suggests that it may be a primary source rather than secondary organic aerosol (SOA). PM 0.1 elemental carbon (EC) was dominated by diesel fuel combustion with less than 15% contribution from other sources. All sources besides wood smoke exhibited relatively constant seasonal source contributions to PM 0.1 OC reflecting approximately constant emissions over the annual cycle. Annual-average source contributions to PM 0.1 OC calculated with traditional molecular markers were similar to the source contributions calculated with the modified molecular markers that account for volatility and reactivity.