Abstract
Most worldwide industrial wastewater, including in China, is still directly discharged to aquatic environments without adequate treatment. Because of a lack of data and few methods, the relationships between pollutants discharged in wastewater and those in surface water have not been fully revealed and unsupervised machine learning techniques, such as clustering algorithms, have been neglected in related research fields. In this study, real-time monitoring data for chemical oxygen demand (COD), ammonia nitrogen (NH3-N), pH, and dissolved oxygen in the wastewater discharged from 2213 factories and in the surface water at 18 monitoring sections (sites) in 7 administrative regions in the Yangtze River Basin from 2016 to 2017 were collected and analyzed by the partitioning around medoids (PAM) and expectation–maximization (EM) clustering algorithms, Welch t-test, Wilcoxon test, and Spearman correlation. The results showed that compared with the spatial cluster comprising unpolluted sites, the spatial cluster comprised heavily polluted sites where more wastewater was discharged had relatively high COD (>100 mg L−1) and NH3-N (>6 mg L−1) concentrations and relatively low pH (<6) from 15 industrial classes that respected the different discharge limits outlined in the pollutant discharge standards. The results also showed that the economic activities generating wastewater and the geographical distribution of the heavily polluted wastewater changed from 2016 to 2017, such that the concentration ranges of pollutants in discharges widened and the contributions from some emerging enterprises became more important. The correlations between the quality of the wastewater and the surface water strengthened as the whole-year data sets were reduced to the heavily polluted periods by the EM clustering and water quality evaluation. This study demonstrates how unsupervised machine learning algorithms play an objective and effective role in data mining real-time monitoring information and highlighting spatio–temporal relationships between pollutants in wastewater discharges and surface water to support scientific water resource management.
Highlights
Except in the most highly developed countries, most worldwide wastewater is treated inadequately before being released to the environment, with negative consequences for human health, economicWater 2019, 11, 1268; doi:10.3390/w11061268 www.mdpi.com/journal/waterWater 2019, 11, 1268 productivity, the quality of freshwater resources, and ecosystems
Wastewater-generating factories and surface water sites in SC, Chongqing Municipality (CQ), Hunan Province (HuN), Henan Province (HeN), Anhui Province (AH), and Jiangsu Province (JS) administrative regions (ARs) were mainly in the same spatial partitioning around medoids (PAM) clusters; Hubei Province (HB) was split between two PAM clusters, with 302 factories and 2 sites (HB1 and HB3) in PAM2 and 20 factories and 1 site (HB2) in PAM3 (Table S4)
More than 33% of the industrial classes of wastewater-generating economic activities discharged effluent with high chemical oxygen demand (COD) and NH3 -N concentrations, regardless of the discharge limits outlined in the discharge standards for pollutants for each industrial class
Summary
Except in the most highly developed countries, most worldwide wastewater is treated inadequately before being released to the environment, with negative consequences for human health, economicWater 2019, 11, 1268; doi:10.3390/w11061268 www.mdpi.com/journal/waterWater 2019, 11, 1268 productivity, the quality of freshwater resources, and ecosystems. In China, factories prefer to be located beside rivers so that they have easy access to water and the generated wastewater can be discharged to the water environment, mostly without adequate treatment. In 2014, China implemented a national strategy to develop the Yangtze River Economic Belt, which accounts for more than 40% of both the national population and GDP and stretches from Yunnan Province in the southwest of China to Shanghai in the east, to boost development in riverside regions and provide new stimuli for China’s slowing economy and, at the same time, to restore and protect the environment [2,3]. Because of a lack of data and few methods, there is no clear picture of how much these enterprises contributed to the river pollution or about how the pollutants in wastewater discharges from specific economic activities are related to the surface water quality in the same region
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