As the main source of fresh water on the Earth’s surface, rivers play an irreplaceable role in human survival and social development. In recent years, with accelerated urbanization and rapid economic development, river pollution has drawn great attention from the government and the public (Reitz et al., 2021;Albergamo et al., 2019;Wijesiri et al., 2019). According to studies, the primary factors responsible for a decrease in river water quality are excessive sewage discharges from industry, households, and agriculture, particularly in developing countries (Reitz et al., 2021;Xu et al., 2019;Xu et al., 2017;Varol et al., 2012). When a river is polluted, it can significantly impact the overall water quality of the surrounding aquatic environment because it plays an essential role in the hydrological cycle (Tong et al., 2017;Han et al., 2020). For this reason, the implementation of accurate source identification and allocation in the management of pollution sources is of paramount importance in assessing and safeguarding the integrity of water resources (Zhang et al., 2020a;Zhang et al., 2017). According to the Bulletin of Ecology and Environment in China in 2022 (MEEC, 2022), 12.1% of surface water was unsuitable for drinking in 2022, Yangtze River Basin, Pearl River Basin, Zhejiang and Fujian piece of rivers, northwest rivers, and southwest rivers water quality is excellent, the Yellow River Basin, Huaihe River Basin, and Liaohe River Basin water quality is good, the Songhua River Basin and the Haihe River Basin for light contamination.
Principal component analysis/factor analysis (PCA/FA) are traditional mathematical and statistical methods. PCA/FA linearly transforms variables using orthogonal transformation. These techniques have a proven efficacy in qualitatively identifying sources of pollution. However, they lack the capacity to accurately determine the precise contributions of sources (Yu et al., 2022;Xiao et al., 2016;Jabbar and Grote, 2019;Le et al., 2023;Muangthong and Shrestha, 2015;Ma et al., 2020). Positive matrix factorization (PMF), chemical mass balance, Unmix, absolute principal component score-multiple linear regression (APCS-MLR), and global optimal inverse models which are models use statistical methods and mathematical algorithms to estimate the contributions of different pollutant sources and provide valuable insights into the sources of pollution in soil, air, and water (Cheng et al., 2020;Zhang et al., 2020b;Chen et al., 2023;Gholizadeh et al., 2016;Liu et al., 2019;Li et al., 2021;Cho et al., 2022;Yu et al., 2022). The APCS-MLR models have been commonly used to quantify pollution sources in different water bodies, such as lakes (Yang et al., 2020), rivers (Cheng et al., 2020;Gholizadeh et al., 2016), coastal water (Ma et al., 2020;Zhou et al., 2022), and groundwater (Zhang et al., 2020a;Li et al., 2021).
In recent years, numerous researches have demonstrated the stability of these models, however, the APCS-MLR model proves to be more effective in handling data sets with parameters of varying magnitudes (Cheng et al., 2020;Zhang et al., 2020b;Li et al., 2021;Cho et al., 2022;Yu et al., 2022;Chen et al., 2023). Zhang et al. (2020b) analyzed a water quality dataset consisting of 15 parameters collected from eight sampling sites in the tributaries and mainstream of the Min River. The APCS-MLR model was employed to identify potential sources of pollution and allocate their respective contributions. Similarly, the five sources of river water contamination that Chen et al. (2023) identified were as follows, with the contribution ratios listed in descending order: the geogenic process (24%) is more prevalent in the Xinbian River of Anhui Province, eastern China than agricultural activities (21%), sources related to chicken farming (17%), residential pollution (9%) and transportation pollution (5%). These studies have important implications for regional water resources management.
Shanghai is located in East China, on the west coast of the Pacific Ocean and the eastern edge of the Asian continent, part of the alluvial plain of the Yangtze River Delta. Previous studies have shown that deteriorated river water quality significantly affects the economic development and the health of people in this area (Wu, 2023;Xu and Yin, 2003). In recent years, the water systems of Shanghai have been greatly improved by the execution of a series of water-management plans. Since 2017, Shanghai has started a new phase of large-scale water environment management. In 2018, the black odor of small and medium-sized rivers was essentially eliminated, and in 2020, the inferior Grade V water bodies were essentially eliminated.
In the Huangpu River Waterfront Area Construction Plan (2018–2035), a spatial development strategy of "two cores and multiple nodes" is intended for the area along the Huangpu River, with each portion being staggered and synergistic, and the core functions of finance, innovation, and culture with global competitiveness being built out in a cluster fashion in the most significant sections. Upstream The Xupu Bridge to Dianshan Lake segment strengthens the strategic ecological conservation function basis and effectively integrates the functions of life, recreation, culture, and innovation industry. The core section (Yangpu Bridge to Xupu Bridge) concentrates on carrying the core functions of finance, business, culture, commerce, and recreation of the international metropolis, and provides public activity space with global influence. The downstream section (Wusongkou to Yangpu Bridge) provides development space for innovative functions based on regional transformation and upgrading, and strengthens the integration of ecological and public functions and living functions.
Wu (2023) used the chemical oxygen demand, total phosphorus, dissolved oxygen, ammonium nitrogen and biochemical oxygen demand and showed that after the river remediation work of Yangpu District in 2017, the main pollutants in the river converted from ammonium nitrogen to total phosphorus, indicating that the river remediation improves the self-purification ability of the river, which had a remarkable influence on the water quality. However, no study has been conducted to characterize online monitoring statistics of the whole river. Therefore, this study selected the Huangpu River, known as the "Mother River of Shanghai", as the study area, and used mathematical and statistical analysis techniques PCA/FA combined with the APCS-MLR model to (1) evaluate the seasonal characteristics of river water quality in the study area, (2) identify potential sources of primary water elements, and (3) quantify the source inputs and contribution variables for each water quality parameter. The findings of this study are expected to help assess the water quality and potential sources of pollution in the Huangpu River in Shanghai and enable stakeholders to more effectively manage pollutants entrance into the river and improve surface water quality in this area.