Urban green space (UGS) is important in urban systems, as it benefits economic development, ecological conservation, and living conditions. Many studies have evaluated the economic, ecological, and social value of UGS worldwide, and spatial optimization for UGS has been carried out to maximize its value. However, few studies have simultaneously examined these three values of UGS in one optimization system. To fill this gap, this study evaluated the economic value of UGS in terms of promoting housing prices, its ecological value through the relief of high land surface temperature (LST), and its social value through the provision of recreation spaces for residents within a 255 m distance. Subsequently, these three values were set as objectives in a genetic algorithm (GA)-based multi-objective optimization (MOP) system. Shenzhen was taken as the case study area. The results showed that the influencing distance of UGS in Shenzhen for house prices was 345 m, and the influencing distance of UGS for LST was 135 m. Using MOP, the Pareto solutions for increasing UGS were identified and presented. The results indicate that MOP can simultaneously optimize UGS’s economic, ecological, and social value.

Many studies have established the correlation between crop residual burning (CRB) and air pollutants such as particulate matter (PM) pollutants. However, few studies have compared CRB’s correlations with all major air pollutants, including PM with aerodynamic diameters less than 10µm (PM10) and 2.5 µm (PM2.5), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO), during open-field CRB seasons. This study monitored daily CRB spots from 2015 to 2016 in China, together with daily concentrations of PM2.5, PM10, CO, SO2, NO2, and 8 h average O3 provided by the China National Environmental Monitoring Center. Temporal changes in air pollutant concentration at the provincial level and spatial contributions of CRB to air pollution at the monitoring-site level were analyzed. The results indicated that the decrease in CRB from 2015 to 2016 probably led to a decrease in the mean air pollutant concentration and maxima; it also probably led to more outliers at the provincial level in the CRB seasons, especially in Heilongjiang, Jilin, and Liaoning. Spatial contributions suggest that the Heilongjiang, Jilin, and Liaoning provinces experienced a regional increase in the concentrations of almost all major air pollutants during the CRB seasons. In Heilongjiang, Jilin, and Liaoning, 67.88%, 72.12%, 80.00%, 77.58%, and 69.09% of the monitoring sites recorded higher concentrations of PM2.5, PM10, NO2, O3 , and CO, respectively, in the winter CRB season of 2015 compared to other periods. In contrast, in other provinces with fewer and more widely distributed CRB spots, only the monitoring sites close to or on CRB spots experienced increases.