Optimal Designing of Air Quality Monitoring Network around a Port, in Bahia State

Particulate matter in the air ambient as an atmospheric pollutant is widely associated with sundry effects in the human health, mostly related to respiratory and cardiovascular diseases (Fajersztajn, 2013 and Cosselman, 2015). Therefore, in Brazil, particulate matter monitoring is a priority task on Air Quality Management Programs (MMA, 2020). The present study designs an optimal Particulate Matter Monitoring Network (TSP, PM10 and PM2,5) around a future Port to be implemented in Ilhéus city, Bahia’s state, Brazil. The optimal station locations were determined in order to evaluate the neighboring air quality impacts, mostly provoked by iron ore, clinker, fertilizer, soy and others solid bulks handling and storage activities. It were applied optimal spatial methodology to obtain representative monitoring data and accurate coverage in the area of interest. The Air Quality Monitoring Network design were determinate based on dispersion modeling of pollutants emitted by the Port and also on the application of Figure Of Merit (FOM) and Sphere Of Influence (SOI) concepts, developed LIU et al. (1986). AERMOD model was used for dispersion simulation, in a study area of 2.450 km², with three years of meteorological data (from 2008 to 2010) collected from Jorge Amado Airport. It were allocated ten thousand receptors on a square grid of 500 meters. Receptors were positioned on the previous air quality monitoring network, in the neighboring location of the enterprise directly affected area and its surroundings. It was also incorporated topography influence by Digital Model SRTM3/NASA. Results has shown that the Port’s emissions will contribute with hourly average up to 13,4 μg/m³ of PM2,5, 100 μg/m³ of PM10 and 280 μg/m³ of TSP. The achieved levels obtained for TSP and PM10 exceeded the standards established by the CONAMA’S Resolution No. 491/19. The hourly maximum concentrations on the neighborhood of directly affected area has shown three times greater than those found in the former Air Quality Monitoring Stations. In the surroundings, the concentrations found were five times greater than those of the old monitoring point. The previous Air Quality Monitoring Network did not contemplate the locations with greater correlation between population exposure data and pollutant highs concentrations, with that being confirmed by the obtained FOMs. It were selected four Monitoring Stations to be installed on the New Monitoring Network with spatial correlation coefficients over 70% between the station and the receptor, besides the addition of a fifth monitoring station for background concentrations.