Gaps in regional water treatment performance : Integrating remote sensing and Google Earth Engine for assessment of water quality and treatment performance in Dong Nai province, Vietnam

Abstract

Securing safe drinking water is fundamental to human health and sustaining production systems. However, pressures from climate change, population growth, and industrial productions are deteriorating global water resources, including those in Vietnam. Dong Nai province, a significant contributor to the national economy, is facing difficulties in maintaining safe domestic water due to incompetent water treatment. Moreover, treatment performances are inconsistent among districts, leading to inequality of safe drinking water for all residents. Hence, achieving balanced improvement in treatment performance is necessary for the sustainable growth of Dong Nai. Addressing these challenges requires comprehensive water quality assessments. Most research, particularly in Vietnam, incorporate conventional methods that are often costly and labour intensive. Advance technology has enabled convenient and cost-effective research methods and tools, such as remote sensing technique and Google Earth Engine (GEE). In response to the challenges in Dong Nai, this thesis employs both remote sensing and GEE for assessment of water quality and treatment process. It further verifies the regional development gaps by comparing performances between a city and a rural district. A research question was set for the study: Can remote sensing technique and GEE be used for assessment of water quality and treatment performance in differently developing areas? Spatial analysis was conducted on primary water sources in the study areas: Dong Nai River, Nui Le Lake, and Gia Ui Lake. Dewantoro et al. (2024)’s research methodology was adopted as the analytical framework. This approach produced spatial and temporal turbidity estimations of observed water bodies from 2022 to 2024. It further brought reliable data for evaluating treatment performance, thereby proving the feasibility of remote sensing and GEE. The turbidity analysis revealed clear variations between two districts, with Dong Nai River displaying the most variations. These findings indicate contamination trends that require immediate controls. When compared with treated records, differences in treatment performances become apparent, with Bien Hoa demonstrating an overall better performance than Xuan Loc district. These disparities highlight existing development gaps that demand effective improvement. Importantly, this thesis made a key contribution for demonstrating the effectiveness of remote sensing technique and GEE for related assessments, along with their potential to support evidence based decision-making.