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Haze pollution is a key obstacle for environmental management faced by China and many other developing countries. The survey on residential families' economic losses and willingness to pay (WTP) are regarded as an essential reference for the implementation of environmental policies for haze treatment. For Jiangsu province of China, the authors of this paper first conducted three qualitative interviews with respectively meteorologists, meteorological administrators, and residents, a questionnaire was then elaborately designed, and subsequent surveys of 1123 families were administered in Jiangsu province. Further, the authors investigated measurements of direct economic losses by using the contingent valuation method (CVM) and explored influential factors of WTP by utilizing the binary logistic regression. From this survey, the estimated total economic loss incurred by haze disasters and total treatment cost for haze-related diseases were respectively 22.38 billion (in RMB) and 8.4 billion for Jiangsu province. 55.9% of residential families were willing to pay 11.6 billion RMB annually (51.97% of total loss) for haze treatment, leaving a shortage of 11.05 billion RMB, which the government is responsible to pay. These findings provide empirical information reflecting the opinions of communities and residential families, useful for the governments and industrial sectors to design environmental policies to meet the requirements of the public and control environmental pollution in an effective way to achieve sustainable development.

In an attempt to investigate the impact of major events on urban air quality in terms of the extent, duration and spatial scope, data on the daily air quality index and the concentrations of individual pollutants are collected in 140 cities of China from January 2, 2015, to November 28, 2017. Based on a spatial differences-in-differences, the impact of major events, such as political conferences, sporting events at the national level, on urban air quality in the dimensions of time and space are explored. It is concluded that major events not only affected the air quality of the host city, but also exercised influence on the air quality of the surrounding areas. Recommendations for mitigating the impact of major events on urban air quality have been proposed, such as establish regional atmospheric environment management system and formulate regional unified standards for pollutant discharge, industrial access and law enforcement. [ABSTRACT FROM AUTHOR]

Gasoline is one of the most consumed light petroleum products in transportation and other industries. This paper proposes a method for optimizing gasoline octane loss using data analysis technology aimed at optimizing the production process and minimizing the loss of gasoline octane. Firstly, the data are screened and the high-dimensional data are reduced to construct the neural network prediction model optimized by genetic algorithm. After utilizing the model for prediction, the optimal operating condition is achieved. Secondly, ensuring that the gasoline emission meets the standard, the octane loss is reduced by adjusting the operating variables. Thirdly, actual data are collected and calculated to obtain the main operating variables and their optimal operating conditions of a petrochemical company affecting the catalytic cracking gasoline S-Zorb unit, thus providing companies using S-Zorb units with reference data for optimizing gasoline catalytic cracking processes. Fourthly, the superiority of the proposed method was verified by comparing it with the other methods. This paper intends to contribute to better modeling the progress of gasoline catalytic cracking by adequately considering the impact of multiple factors, improving the quality of refined oil products of chemical enterprises, saving the economic cost of chemical enterprises, and protecting the atmospheric environment. [ABSTRACT FROM AUTHOR]

Based on the related impact factors of precipitation anomaly referred in previous research, eight atmospheric circulation indicators in pre-winter and spring picked out by correlation analysis as the independent variables and the hazard levels of drought/flood sudden alternation index (DFSAI) as the dependent variables were used to construct the nonlinear and nonparametric classification and regression tree (CART) for the threshold determination and hazard evaluation on bimonthly and monthly scales in Huaihe River basin. Results show that the spring indicators about Arctic oscillation index (AOI_S), Asia polar vortex area index (APVAI_S), and Asian meridional circulation index (AMCI_S) were extracted as the three main impact factors, which were proved to be suitable for the hazard levels assessment of the drought/flood sudden alternation (DFSA) disaster based on bimonthly scale. On monthly scale, AOI_S, northern hemisphere polar vortex intensity index in pre-winter (NHPVII_PW), and AMCI_S are the three primary variables in hazard level prediction of DFSA in May and June; NHPVII_PW, AMCI_PW, and AMCI_S are for that in June and July; NHPVII_PW and EASMI are for that in July and August. The type of the disaster (flood to drought/drought to flood/no DFSA) and hazard level under different conditions also can be obtained from each model. The hazard level and type were expressed by the integer from − 3 to 3, which change from the high level of disaster that flood to drought (level − 3) to the high level of the reverse type (level 3). The middle number 0 represents no DFSA. The high levels of the two sides decrease progressively to the neutralization (level 0). When AOI_S less than − 0.355, the disaster of the quick turn from drought to flood is more apt to happen (level 1) on bimonthly scale; when AOI_S less than − 1.32, the same type disaster may occur (level 2) in May and June on monthly scale. When NHPVII_PW less than 341.5, the disaster of the quick turn from flood to drought will occur (level − 1) in June and July on monthly scale. By this analogy, different hazard types and levels all can be judged from the optimal models. The corresponding data from 2011 to 2015 were selected to verify the final models through the comparison between the predicted and actual levels, and the models of M1 (bimonthly scale), M2, and M3 (monthly scale) were proved to be acceptable by the prediction accuracy rate (compared the predicted with the observed levels, 73%, 11/15). The proposed CART method in this research is a new try for the short-term climate prediction. [ABSTRACT FROM AUTHOR]