Abstract Fine particulate matter (PM2.5) poses significant health risks, particularly to children; yet, ambient air quality studies in school environments across Kumasi, Ghana, remain limited. This study utilized low-cost Airnote sensors and meteorological data (wind speed and wind direction) from the ERA5-Land Reanalysis to assess levels of PM2.5 pollution across six senior high schools in Kumasi between 2022 and 2023, capturing spatial and seasonal variability during both the dry and wet seasons. Results revealed an annual median PM2.5 concentration of 17.18 <InlineEquation ID="IEq1"> <EquationSource Format="TEX">$\mu$</EquationSource> <EquationSource Format="MATHML"><math> <mi>μ</mi> </math></EquationSource> </InlineEquation>g/m<InlineEquation ID="IEq2"> <EquationSource Format="TEX">$^3$</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mn>3</mn> </mmultiscripts> </math></EquationSource> </InlineEquation>, exceeding the WHO annual guideline of 5 <InlineEquation ID="IEq3"> <EquationSource Format="TEX">$\mu$</EquationSource> <EquationSource Format="MATHML"><math> <mi>μ</mi> </math></EquationSource> </InlineEquation>g/m<InlineEquation ID="IEq4"> <EquationSource Format="TEX">$^3$</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mn>3</mn> </mmultiscripts> </math></EquationSource> </InlineEquation>. Diurnal patterns exhibited bimodal peaks aligned with morning and evening commuting and domestic activities, driven by traffic emissions, biomass burning, and informal waste burning. Pollution levels were notably elevated during weekdays and Saturdays but lower on Sundays. Median concentrations were highest at SHS E (20.91 <InlineEquation ID="IEq5"> <EquationSource Format="TEX">$\mu$</EquationSource> <EquationSource Format="MATHML"><math> <mi>μ</mi> </math></EquationSource> </InlineEquation>g/m<InlineEquation ID="IEq6"> <EquationSource Format="TEX">$^3$</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mn>3</mn> </mmultiscripts> </math></EquationSource> </InlineEquation>), followed by SHS A (19.22 <InlineEquation ID="IEq7"> <EquationSource Format="TEX">$\mu$</EquationSource> <EquationSource Format="MATHML"><math> <mi>μ</mi> </math></EquationSource> </InlineEquation>g/m<InlineEquation ID="IEq8"> <EquationSource Format="TEX">$^3$</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mn>3</mn> </mmultiscripts> </math></EquationSource> </InlineEquation>), SHS F (18.16 <InlineEquation ID="IEq9"> <EquationSource Format="TEX">$\mu$</EquationSource> <EquationSource Format="MATHML"><math> <mi>μ</mi> </math></EquationSource> </InlineEquation>g/m<InlineEquation ID="IEq10"> <EquationSource Format="TEX">$^3$</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mn>3</mn> </mmultiscripts> </math></EquationSource> </InlineEquation>), and SHS D (16.71 <InlineEquation ID="IEq11"> <EquationSource Format="TEX">$\mu$</EquationSource> <EquationSource Format="MATHML"><math> <mi>μ</mi> </math></EquationSource> </InlineEquation>g/m<InlineEquation ID="IEq12"> <EquationSource Format="TEX">$^3$</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mn>3</mn> </mmultiscripts> </math></EquationSource> </InlineEquation>), while SHS B (15.32 <InlineEquation ID="IEq13"> <EquationSource Format="TEX">$\mu$</EquationSource> <EquationSource Format="MATHML"><math> <mi>μ</mi> </math></EquationSource> </InlineEquation>g/m<InlineEquation ID="IEq14"> <EquationSource Format="TEX">$^3$</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mn>3</mn> </mmultiscripts> </math></EquationSource> </InlineEquation>) and SHS C (12.76 <InlineEquation ID="IEq15"> <EquationSource Format="TEX">$\mu$</EquationSource> <EquationSource Format="MATHML"><math> <mi>μ</mi> </math></EquationSource> </InlineEquation>g/m<InlineEquation ID="IEq16"> <EquationSource Format="TEX">$^3$</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mn>3</mn> </mmultiscripts> </math></EquationSource> </InlineEquation>) recorded the lowest levels. Seasonal differences were pronounced: the dry season showed significantly higher pollution (mean = 26.82 <InlineEquation ID="IEq17"> <EquationSource Format="TEX">$\mu$</EquationSource> <EquationSource Format="MATHML"><math> <mi>μ</mi> </math></EquationSource> </InlineEquation>g/m<InlineEquation ID="IEq18"> <EquationSource Format="TEX">$^3$</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mn>3</mn> </mmultiscripts> </math></EquationSource> </InlineEquation>) than the wet season (mean = 13.18 <InlineEquation ID="IEq19"> <EquationSource Format="TEX">$\mu$</EquationSource> <EquationSource Format="MATHML"><math> <mi>μ</mi> </math></EquationSource> </InlineEquation>g/m<InlineEquation ID="IEq20"> <EquationSource Format="TEX">$^3$</EquationSource> <EquationSource Format="MATHML"><math> <mmultiscripts> <mrow /> <mrow /> <mn>3</mn> </mmultiscripts> </math></EquationSource> </InlineEquation>), owing to reduced rainfall and limited atmospheric dispersion. Conditional Bivariate Probability Function (CBPF) analysis and HYSPLIT back-trajectory modeling identified dominant pollution sources, including nearby traffic corridors, domestic combustion activities, unmanaged waste burning, and long-range Saharan dust transport, with clear seasonal shifts in source directionality. Spatial variability in PM2.5 concentrations was further influenced by land-use characteristics and topography surrounding each school. These findings underscore the need for localized air quality management strategies, particularly in vulnerable environments like schools, to mitigate health risks and enhance urban air quality governance. Graphic Abstract

Spatiotemporal Assessment of PM_2.5 in Senior High Schools in Kumasi, Ghana using Low-Cost Sensors

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2025年11月19日

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Victoria Owusu-Tawiah,
Thompson Annor,
Edmund I. Yamba,
James Nimo,
Cosmos Senyo Wemegah,
Collins Gameli Hodoli,
Desmond Osei-Tutu,
Daniel Amponsah,
Allison Felix Hughes,
Daniel M. Westervelt

摘要

Abstract

Fine particulate matter (PM_2.5) poses significant health risks, particularly to children; yet, ambient air quality studies in school environments across Kumasi, Ghana, remain limited. This study utilized low-cost Airnote sensors and meteorological data (wind speed and wind direction) from the ERA5-Land Reanalysis to assess levels of PM_2.5 pollution across six senior high schools in Kumasi between 2022 and 2023, capturing spatial and seasonal variability during both the dry and wet seasons. Results revealed an annual median PM_2.5 concentration of 17.18 $\mu$ $μ$ g/m $^3$ $^{3}$ , exceeding the WHO annual guideline of 5 $\mu$ $μ$ g/m $^3$ $^{3}$ . Diurnal patterns exhibited bimodal peaks aligned with morning and evening commuting and domestic activities, driven by traffic emissions, biomass burning, and informal waste burning. Pollution levels were notably elevated during weekdays and Saturdays but lower on Sundays. Median concentrations were highest at SHS E (20.91 $\mu$ $μ$ g/m $^3$ $^{3}$ ), followed by SHS A (19.22 $\mu$ $μ$ g/m $^3$ $^{3}$ ), SHS F (18.16 $\mu$ $μ$ g/m $^3$ $^{3}$ ), and SHS D (16.71 $\mu$ $μ$ g/m $^3$ $^{3}$ ), while SHS B (15.32 $\mu$ $μ$ g/m $^3$ $^{3}$ ) and SHS C (12.76 $\mu$ $μ$ g/m $^3$ $^{3}$ ) recorded the lowest levels. Seasonal differences were pronounced: the dry season showed significantly higher pollution (mean = 26.82 $\mu$ $μ$ g/m $^3$ $^{3}$ ) than the wet season (mean = 13.18 $\mu$ $μ$ g/m $^3$ $^{3}$ ), owing to reduced rainfall and limited atmospheric dispersion. Conditional Bivariate Probability Function (CBPF) analysis and HYSPLIT back-trajectory modeling identified dominant pollution sources, including nearby traffic corridors, domestic combustion activities, unmanaged waste burning, and long-range Saharan dust transport, with clear seasonal shifts in source directionality. Spatial variability in PM_2.5 concentrations was further influenced by land-use characteristics and topography surrounding each school. These findings underscore the need for localized air quality management strategies, particularly in vulnerable environments like schools, to mitigate health risks and enhance urban air quality governance.

Graphic Abstract

Spatiotemporal Assessment of PM2.5 in Senior High Schools in Kumasi, Ghana using Low-Cost Sensors

摘要

Spatiotemporal Assessment of PM_2.5 in Senior High Schools in Kumasi, Ghana using Low-Cost Sensors