Spatial-temporal Variation and Local Source Identification of Air Pollutants in a Semi-urban Settlement in Nigeria Using Low-cost Sensors

Abstract

Low-cost sensors were deployed at five locations in a growing, semi-urban settlement in southwest Nigeria between June 8 and July 31, 2018 to measure particulate matter (PM2.5 and PM10), gaseous pollutants (CO, NO, NO 2, O3 and CO2), and meteorological variables (air temperature, relative humidity, wind speed and wind-direction). The spatial and temporal variations of measured pollutants were determined, and the probable sources of pollutants were inferred using conditional bivariate probability function (CBPF). Hourly PM2.5 and PM10 concentrations ranged from 20.7 ± 0.7 to 36.3 ± 1.6 µgm -3 and 47.5 ± 1.5 to 102.9 ± 5.6 µgm -3 , respectively. Hourly gaseous pollutant concentrations 21 ranged from 348 ± 132 to 542 ± 200 ppb CO, 21.5 ± 7.2 ppb NO2 and 57.5 ± 11.3 to 64.4 ± 14.0 ppb O 3. Kruskal-Wallis ANOVA on ranks determined statistically significant spatial differences in the hourly-average pollutant concentrations. Diel variation analyses indicated that CO2, PM2.5, and PM10 24 peaked in the early hours of most days, O 3 at noon while NO, NO2, and CO peaked in the evening. 25 Most pollutants were of anthropogenic origins and exhibited the highest contributions from the 26 southwest at most sampling locations. There were strong similarities between pollutants source 27 contribution at two of the monitoring sites that were in residential areas with a frequently used paved 28 road. Mitigation strategies need to be established to avoid further deterioration of ambient air quality that negatively affect public health.

Spatio-temporal statistical analysis of PM1 and PM2.5 concentrations and their key influencing factors at Guayaquil city, Ecuador

“…Perez-Martinez and Miranda (2015) [PM10-1 h] found positive and statistically significant relationships between PM and temperature using CCF. Wang and Ogawa (2015) 24 h], Owoade et al (2021) [PM2.5-24 h] all found negative correlations for temperature and PM 2.5 when applying complex statistical techniques. The main explanation for these correlations is attributed to temperature-related atmospheric convections: an increase in the air temperature increases the atmospheric turbulence (vertical diffusion depends on an increase in ambient temperatures at the urban boundary layer), which accelerates the dispersion, diffusion, and dilution of pollutants.…”

“…However, one positive correlation was found for PM 2.5 -1 h-rainy with a lag of 12 h, suggesting that the correlation between PM and RH could have both positive and negative directions. Ul-Saufie et al (2011 [PM 10 -24 h], Deng et al (2022) [PM 2.5 -24 h], Owoade et al (2021) [PM 2.5 -24 h], Li et al (2022) [PM 2.5 -24 h], Alvarez et al ( 2022) [PM 2.5 -24 h] reported positive relationships between PM and RH performing MLR and other more complex statistical approaches. The reason is that PM 2.5 attaches to water vapour when the relative humidity is high (hygroscopic growth of the particles) and so particulate pollutants tend to cluster, and environmental quality worsens.…”

“…Determining the spatial distribution of an air, soil or waterborne contaminant by its abiotic factors can help to develop an understanding of its background sources and influencing factors and is important for environmental and health risk evaluation (Ambade et al 2021a(Ambade et al ,b, 2022aBisht et al 2022;Gupta et al 2022;Ambade and Sethi, 2021;Kurwadkar et al 2021;Maharjan et al 2021). This explains the recent expansion of research on the spatiotemporal analysis of PM i (Deng et al 2022;Han et al 2022;Li et al 2022;Wang et al 2022a,b;Zhang et al 2022;Zhao et al 2022;Owoade et al 2021;Zhou and Lin 2019). This also applies in Latin America (Carmona, et al 2020;Encalada-Malca et al 2021;Chiquetto et al 2020), but there is a lack of research on Ecuadorian problems.…”

“…The spatio-temporal analysis of PM i and their influencing factors can be performed following a myriad of available statistical techniques, varying in their complexity, that are chosen according to the aim of the study (Carmona et al 2020;Deng et al 2022;Han et al 2022;Li et al 2022;Wang et al 2022a,b;Zhang et al 2022;Zhao et al 2022;Owoade et al 2021;Zhou and Lin 2019). Multiple linear regression (MLR) is one statistical technique applied in air quality analyses to define linear statistical relationships between PM concentrations and meteorological and anthropogenic variables (Morantes et al 2019;Kozakova et al 2017;Nazif et al 2016).…”

Study on Droplet Dispersion Influenced by Ventilation and Source Configuration in Classroom Settings Using Low-cost Sensor Network

“…However, only a few experimental studies have been conducted on respiratory droplet dispersion which is critical in validating numerical simulations and designing optimum ventilation systems to mitigate virus transmission between indoor occupants, since current heating, ventilation, and air conditioning (HVAC) settings are designed for thermal comfort, filtration of outdoor contaminant, and energy saving, but not for minimizing the viral dispersion inside the room. Moreover, this type of study requires spatiotemporal air monitoring, which can be the paradigm of the low-cost sensor technology raised in recent years (Jayaratne et al, 2020;Owoade et al, 2021;Rai et al, 2017). Many low-cost sensors studies dedicated the effort to indoor monitoring (Chojer et al, 2020).…”