Hong Yao

The number of surface water pollution accidents (abbreviated as SWPAs) has increased substantially in China in recent years. Estimation of economic losses due to SWPAs has been one of the focuses in China and is mentioned many times in the Environmental Protection Law of China promulgated in 2014. From the perspective of water bodies’ functions, pollution accident damages can be divided into eight types: damage to human health, water supply suspension, fishery, recreational functions, biological diversity, environmental property loss, the accident’s origin and other indirect losses. In the valuation of damage to people’s life, the procedure for compensation of traffic accidents in China was used. The functional replacement cost method was used in economic estimation of the losses due to water supply suspension and loss of water’s recreational functions. Damage to biological diversity was estimated by recovery cost analysis and damage to environmental property losses were calculated using pollutant removal costs. As a case study, using the proposed calculation procedure the economic losses caused by the major Songhuajiang River pollution accident that happened in China in 2005 have been estimated at 2263 billion CNY. The estimated economic losses for real accidents can sometimes be influenced by social and political factors, such as data authenticity and accuracy. Besides, one or more aspects in the method might be overestimated, underrated or even ignored. The proposed procedure may be used by decision makers for the economic estimation of losses in SWPAs. Estimates of the economic losses of pollution accidents could help quantify potential costs associated with increased risk sources along lakes/rivers but more importantly, highlight the value of clean water to society as a whole.

Understanding historical accidents is important for accident prevention and risk mitigation; however, there are no public databases of pollution accidents in China, and no detailed information regarding such incidents is readily available. Thus, 653 representative cases of surface water pollution accidents in China were identified and described as a function of time, location, materials involved, origin, and causes. The severity and other features of the accidents, frequency and quantities of chemicals involved, frequency and number of people poisoned, frequency and number of people affected, frequency and time for which pollution lasted, and frequency and length of pollution zone were effectively used to value and estimate the accumulated probabilities. The probabilities of occurrences of various types based on origin and causes were also summarized based on these observations. The following conclusions can be drawn from these analyses: (1) There was a high proportion of accidents involving multi-district boundary regions and drinking water crises, indicating that more attention should be paid to environmental risk prevention and the mitigation of such incidents. (2) A high proportion of accidents originated from small-sized chemical plants, indicating that these types of enterprises should be considered during policy making. (3) The most common cause (49.8 % of the total) was intentional acts (illegal discharge); accordingly, efforts to increase environmental consciousness in China should be enhanced.

Environmental safety in multi-district boundary regions has been one of the focuses in China and is mentioned many times in the Environmental Protection Act of 2014. Five types were categorized concerning the risk sources for surface water pollution in the multi-provincial boundary region of the Taihu basin: production enterprises, waste disposal sites, chemical storage sites, agricultural non-point sources and waterway transportations. Considering the hazard of risk sources, the purification property of environmental medium and the vulnerability of risk receptors, 52 specific attributes on the risk levels of each type of risk source were screened out. Continuous piecewise linear function model, expert consultation method and fuzzy integral model were used to calculate the integrated risk indexes (RI) to characterize the risk levels of pollution sources. In the studied area, 2716 pollution sources were characterized by RI values. There were 56 high-risk sources screened out as major risk sources, accounting for about 2% of the total. The numbers of sources with high-moderate, moderate, moderate-low and low pollution risk were 376, 1059, 101 and 1124, respectively, accounting for 14%, 38%, 5% and 41% of the total. The procedure proposed could be included in the integrated risk management systems of the multi-district boundary region of the Taihu basin. It could help decision makers to identify major risk sources in the risk prevention and reduction of surface water pollution.

Ten metals were analyzed in samples collected in three seasons (the dry season, the early rainy season, and the late rainy season) from two rivers in China. No observed toxic effect concentrations were used to estimate the risks. The possible sources of the metals in each season, and the dominant source(s) at each site, were assessed using principal components analysis. The metal concentrations in the area studied were found, using t-tests, to vary both seasonally and spatially (P = 0.05). The potential risks in different seasons decreased in the order: early rainy season > dry season > late rainy season, and Cd was the dominant contributor to the total risks associated with heavy metal pollution in the two rivers. The high population and industrial site densities in the Taihu basin have had negative influences on the two rivers. The river that is used as a source of drinking water (the Taipu River) had a low average level of risks caused by the metals. Metals accumulated in environmental media were the main possible sources in the dry season, and emissions from mechanical manufacturing enterprises were the main possible sources in the rainy season. The river in the industrial area (the Wusong River) had a moderate level of risk caused by the metals, and the main sources were industrial emissions. The seasonal and spatial distributions of the heavy metals mean that risk prevention and mitigation measures should be targeted taking these variations into account.