Abstract: Ambient air pollution is the most important environmental factor impacting human health. Exposure to unsafe levels of air pollutants such as particulate matter has been linked to increased risk of respiratory and cardiovascular diseases. Cities present unique air quality challenges, being areas that typically have higher concentrations of air pollutants and exposed population. In such urban areas, green and blue infrastructure has shown potential for alleviating environmental stressors, including air pollution and heat wave abatement. However, such solutions must be correctly designed in order to maximize mitigation and not inadvertently increase pollutant exposure. This study utilizes low-cost sensors to examine the relationship between green and blue infrastructure and a series of environmental parameters. While previous studies have investigated green infrastructure and air quality mitigation, few have employed low-cost sensors to obtain highly localized data to demonstrate this relationship. In this study, as a part of NERC-FAPESP funded GreenCities project, low-cost sensors were validated through lab testing and then deployed in the field in locations in São Paulo, Brazil. The locations selected for this study allow for a comparison of built versus green and blue areas. Lidar data was used to create a quantitative profile of each site based on Normalized Difference Vegetation Index (NDVI) and leaf area index. Distance to the closest roadways was also measured for each sensor location. These profiles are analyzed against the data obtained through the low-cost sensors, considering both meteorological (temperature, humidity and pressure) and particulate matter (PM1.0, PM2.5 and PM10) parameters. Data collection for this study is ongoing, but this analysis focuses on data collected during the period from August through December of 2023. Preliminary findings show that the sites with higher NDVI values had higher humidity. The impact of green and blue infrastructure on particulate matter and temperature is mixed, with a mitigating impact observed only at one of the two sites for each variable.

Keywords: Low-cost sensors, particulate matter, air quality, green infrastructure, cities