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UrduThe research team, which included two University at Albany scientists from the School of Public Health, used low-cost sensors to assess air quality changes due to two distinct events — a large chemical fire and reduced car usage during the COVID lockdown period — in Houston.
Their system detected significant increases in pollution after the fire, as well as a reduction in pollution during COVID lockdown. Through all conditions, the worst air quality was found in low-income and communities of color.
Tracking fine-scale, real-time changes in air pollution could support efforts to protect public health.
“Every year, poor air quality causes millions of premature deaths worldwide while contributing to cardiovascular, respiratory and neurological disease,” explained senior author and UAlbany Empire Innovation Associate Professor of Environmental Health Sciences Kai Zhang. “This burden is felt most strongly by disadvantaged communities. Our findings highlight the utility of low-cost sensor networks, which, if implemented by municipalities, could provide an accurate measure of community-level air quality to improve response measures aimed at protecting residents’ health, especially among vulnerable populations.”
The study focused on particulate matter (PM) — airborne debris that enters our lungs when we breathe. PM is generated by emissions from smokestacks and cars, as well as any activity that kicks up dirt or dust. PM comes in a variety of sizes, typically classified into three “cuts” — PM1, PM2.5 and PM10. Finer particles have been linked to stroke, dementia and Alzheimer’s disease. Inhaling larger particles can trigger asthma and heart attacks.
“Understanding where and when air quality is poor is a critical public health challenge,” said Zhang. “Unfortunately, large regulatory sensor networks tend to have limited monitors, which make it challenging to track temporary peaks and hot spots in air quality that could impact health — such as between neighborhoods in the same city, from one hour to the next. In this study, we turned to low-cost sensors to address this gap.”
Tracking Temporary Pollution Peaks
To track pollution peaks, the research team installed 20 low-cost sensors on roofs of private residences and firehouses throughout Harris County, Texas. The study area included the city of Houston, as well as the Intercontinental Terminals Company (ITC) chemical plant, which was the site of a major explosion and fire that burned for four days in March 2019, impacting air quality in the region.
“Low-cost sensors are an inexpensive alternative to the kind of sensor typically used by regulatory agencies, Zhang said. “Due to their affordability, it is possible to build a network of tens or hundreds of sensors across an urban landscape that simply wouldn’t be possible using a costlier device.”
The sensors went online in the immediate aftermath of the explosion and fire. The timing coincided with the onset of the pandemic and the subsequent lockdown period. The total monitoring period spanned March 2019 to December 2020.
Environmental Disparities of Air Pollution
The data showed that PM was up after the fire and down during lockdown, validating the accuracy and utility of the low-cost system. They also collected demographic data, including information on race, income, education level and linguistic isolation.
By pairing information on PM peaks and concentration hot spots with demographic data, the team found that disadvantaged communities experienced the highest PM levels and greatest health risk.
“This study adds to the large and growing body of evidence that environmental health risks are felt most severely by nonwhite and low-income communities,” said Shao Lin, study co-author and professor of environmental health sciences at UAlbany. “Finer-scale air quality monitoring can be an important tool to not only detect peaks and establish early warning systems to reduce exposure, but it could also help track harmful air quality levels in relation to human health outcomes.”
“For marginalized people, resources like purifiers are too costly to adopt. Plus, exposure is inevitable for those who work outdoors for a living,” said Lin. “These sorts of divides tend to occur along racialized and socioeconomic lines. Identifying areas bearing the greatest burden, as we have mapped in this study, can help pinpoint communities most in need of municipal support, such as access to shelter when air pollution reaches unhealthy levels.”
Pros and Cons of Low-Cost Sensors
While sensors deployed by regulatory agencies provide highly accurate readings, they are often located away from population centers and are spaced too far apart to be used as a tool for measuring fine-scale peaks and hot spots that overlap with people, according to the researchers.
In addition, unlike the low-cost sensors used in this study, not all regulatory sensors screen for all three sizes of PM, which can pose different severities of risk. Finally, data quantity is important for tracking changes in real time. The low-cost sensors provide readings at a higher frequency, making it possible to detect changes on a finer temporal scale. In other words, with more dots to connect, you get a clearer picture of impact and risk.
“The drawback of low-cost sensors is that their measurements are marginally less precise than more expensive models; however, the data quality is more than sufficient for gauging exposure levels of temporary peaks and hot spots that could harm health,” Zhang said. “Low-cost sensors are a feasible option that municipalities could adopt immediately and at scale, to put a finer point on air pollution levels in different neighborhoods.”
The study is now available online and will be published in Science of the Total Environment later this month.
