Artificial Intelligence Analysis Tapped to Manage Water Contamination

          Say my dream job is to work for the New York City Environmental Protection Bureau of Water Supply as the city has voted to have one of the tastiest pipe waters in the nation (Fernandez, 2024). However, like water supplies in other locations, its reputational quality faces an objective decline. Fortunately, New York State has a well-regulated water shed and test management process for delivering quality drinking water to over 9 million residents. Despite decades of government regulations and effort, the water quality has had difficulties. The reasons vary from factors like ill-informed choices taken at that time to use lead pipes in constructing the supply network, which we later learnt bled into the water and made it toxic for human consumption. Other causes included manufacture pollutants of “forever chemicals”, agriculture waste, and other runoffs into catchments. Given any opportunity to work with this agency I would advocate to the bureau management team the benefits of using Artificial Intelligence (AI) to drive responses in both treatments of maintaining water quality and forecast for health responses to communities impacted by contaminants.

          The trend analysis capabilities and categorization power of AI would be a valuable tool to efficiently help the water bureau monitor and identify contaminants that would otherwise compromise the availability of safe reservoirs. Based on samples of data analysis, AI models could pinpoint responses to those communities that need prioritization for education in health risks and treatment to related water supplies (Drinking water quality data for NYC, n.d.). 

          If AI is not employed to tackle the problem of water contamination, it could impact the health and future of communities if lead concentrations should exceed 5 micrograms per liter in school water. Exceeding this level would be disastrous for the safety of students (Department of Health, n.d.) if not adequately detected early. Studies have shown lead, and other contaminants adversely affect growing minds especially in presence of dangerous chemicals such as polyfluoroalkyl (PFAS) which can lead to chronic diseases like cancer (Blackburn 2024). Health crisis that results from poor water supply will strain government budgets and impact the future of the economy because lead sickness and other water contaminants can impact the potential of students to grow up into productive citizens.

The use of Artificial Intelligence would be positive for the water bureau to leverage as we can use it to build models, trained on lead or other type of contaminant data, to speed up study and strategy around tracking water contamination that affect communities and quantify responsive efforts. An additional benefit of using AI agents to help manage water contamination and response is that, not only is it able to categorize if water supply is trending on a safe path, but it can also help unlock unexpected trends and insights in deeper data analysis. The AI Machine Learning model may use Autoregressive Integrated Moving Average (ARIMA) or other algorithm to review years or months of regular testing data, to provide univariate time series forecasting (uCertify, 2024) to provide a picture of combined impact of thousands of PFAS or debilitating contaminants.

          The water management regulatory bodies can also use AI production models, trained with proven hyper parameters in ARIMA or other autoregression algorithms to predict increases in contaminants in public health exposure for needed collaboration with the state’s Department of Health to adequately prepare responses for anticipated number of children in areas exposed to lead pipes, dispatch of mobile clinics in counties with households connected to piped water tainted by agriculture runoffs. The metrics from AI analysis of the water supply will be positive for transparency when made accessible to the public via the water bureau’s website that they can refer to proactively seek treatment for any expected exposure (Environmental Protection Agency, n.d.)

          AI’s extensive modeling capabilities can be adopted to collection of water sampling to help the Bureau of Water Supply with data insights, such as ARIMA’s seasonality analysis to identify trends around time periods where ground water contaminants may increase such as when farmers fertilize crops for increased yields or spraying to treat pests. The resulting trend may help the bureau plan forecast for added state inspectors to visit farms to ensure they are disposing of agriculture waste properly as well as taking mandated steps to prevent fertilizers runoff into bodies of water that feed reservoirs (Pesticide management for farms and Agriculture, 2024).

          The benefits of using AI on the job are a valuable tool to serve the Water Bureau and consuming public effectively. The adoption of Artificial Intelligence and machine learning brings enhanced safety with new data insights and management of responses to treat water contaminations and community illnesses that result. A well-trained AI Machine Learning model could be adopted to detect thousands of water contaminants and trigger levels of responses and government contingencies. Furthermore, the use of AI in water safety standards not only helps workers to make greater impact in our job through better data analysis, but it also benefits partner agencies in health planning, environmental code enforcement and the general public to work as a team, for maintaining a safe and accessible water supply.

References

Blackburn, K. (2024, April 21). We regulate a tiny fraction of the 12,000 “forever chemicals.” There’s a better way. The New York Times.

Department of Health. (n.d.)  Lead Testing of School Drinking Water. (n.d.).

Drinking water quality data for NYC. Environment & Health Data Portal. (n.d.).

https://a816-dohbesp.nyc.gov/IndicatorPublic/data-explorer/drinking-water-quality/?id=2207#display=summaryLinks to an external site.

Environmental Protection Agency. (n.d.). EPA.

https://www.epa.gov/ground-water-and-drinking-water/basic-information-about-lead-drinking-waterLinks to an external site.

Fernandez, C. (2024, August 2). These U.S. states have the best and Worst Tap Water: Washington ranked no. 1 while Arizona came in dead last.

CNBC. https://www.cnbc.com/2024/08/02/us-states-best-worst-tap-water-j-d-power.html

Pesticide management for farms and Agriculture. Department of Environmental Conservation. (n.d.).

uCertify. (n.d.). Build Univariate Time Series Models. Certified Artificial Intelligence Practitioner. https://wilmu.ucertify.com/app/?func=ebook&chapter_no=6