Suchergebnisse

AbstractUsing the finite volume method, a 2‐D water‐sediment coupled model was developed to investigate the transport mechanisms of water and sediment in Poyang Lake, the largest river‐connected lake in China. Simulating water and sediment transport processes in an average water year revealed that the total volume of water and the amount of suspended sediment transported from the five main rivers upstream of Poyang Lake were approximately 1.56 × 1011 m3 and 1.25 × 107 t, respectively. The outputs of water and sediment from the lake to the downstream Yangtze River were approximately 2.17 × 1011 m3 and 1.53 × 107 t, respectively. During the wet season, especially between July and September, nearly 1.06 × 1010 m3 of water and 1.89 × 106 t of suspended sediment were transported upstream from the Yangtze River into the lake due to the high external water levels, which accounted for 4.88% and 12.35%, respectively, of the total annual transport from the lake into the Yangtze River.

At present particulate matter (PM₂.₅) pollution represents a serious threat to the public health and the national economic system in China. This paper optimizes the whitening coefficient in a grey Markov model by a genetic algorithm, predicts the concentration of fine particulate matter (PM₂.₅), and then quantifies the health effects of PM₂.₅ pollution by utilizing the predicted concentration, computable general equilibrium (CGE), and a carefully designed exposure–response model. Further, the authors establish a social accounting matrix (SAM), calibrate the parameter values in the CGE model, and construct a recursive dynamic CGE model under closed economy conditions to assess the long-term economic losses incurred by PM₂.₅ pollution. Subsequently, an empirical analysis was conducted for the Beijing area: Despite the reduced concentration trend, PM₂.₅ pollution continued to cause serious damage to human health and the economic system from 2013 to 2020, as illustrated by various facts, including: (1) the estimated premature deaths and individuals suffering haze pollution-related diseases are 156,588 (95% confidence intervals (CI): 43,335–248,914)) and six million, respectively; and (2) the accumulated labor loss and the medical expenditure negatively impact the regional gross domestic product, with an estimated loss of 3062.63 (95% CI: 1,168.77–4671.13) million RMB. These findings can provide useful information for governmental agencies to formulate relevant environmental policies and for communities to promote prevention and rescue strategies.

At present particulate matter (PM(2.5)) pollution represents a serious threat to the public health and the national economic system in China. This paper optimizes the whitening coefficient in a grey Markov model by a genetic algorithm, predicts the concentration of fine particulate matter (PM(2.5)), and then quantifies the health effects of PM(2.5) pollution by utilizing the predicted concentration, computable general equilibrium (CGE), and a carefully designed exposure–response model. Further, the authors establish a social accounting matrix (SAM), calibrate the parameter values in the CGE model, and construct a recursive dynamic CGE model under closed economy conditions to assess the long-term economic losses incurred by PM(2.5) pollution. Subsequently, an empirical analysis was conducted for the Beijing area: Despite the reduced concentration trend, PM(2.5) pollution continued to cause serious damage to human health and the economic system from 2013 to 2020, as illustrated by various facts, including: (1) the estimated premature deaths and individuals suffering haze pollution-related diseases are 156,588 (95% confidence intervals (CI): 43,335–248,914)) and six million, respectively; and (2) the accumulated labor loss and the medical expenditure negatively impact the regional gross domestic product, with an estimated loss of 3062.63 (95% CI: 1,168.77–4671.13) million RMB. These findings can provide useful information for governmental agencies to formulate relevant environmental policies and for communities to promote prevention and rescue strategies.