The paucity of fine particulate matter (PM2.5) measurements limits estimates of air pollution mortality in Sub-Saharan Africa. If well calibrated, low-cost sensors can provide reliable data especially where reference monitors are unavailable. We evaluate the performance of Clarity Node-S PM monitors against a Tapered element oscillating microbalance (TEOM) 1400a and develop a calibration model in Mombasa, Kenya’s second largest city. As-reported Clarity Node-S data from January 2023 through April 2023 was moderately correlated with the TEOM-1400a measurements (R2 = 0.61) and exhibited a mean absolute error (MAE) of approximately 7.03 µg m–3. Employing three calibration models, namely, multiple linear regression (MLR), gaussian mixture regression (GMR) and random forest (RF) decreased the MAE to 4.28, 3.93, and 4.40 µg m–3 respectively. The R2 value improved to 0.63 for the MLR model but all other models registered a decrease (R2 = 0.44 and 0.60 respectively). Applying the correction factor to a 5-sensor network in Mombasa that was operated between July 2021 and July 2022 gave insights to the air quality in the city. The average daily concentrations of PM2.5 within the city ranged from 12 to 18 µg m–3. The concentrations exceeded the WHO daily PM2.5 limits more than 50% of the time, in particular at the sites nearby frequent industrial activity. Higher averages were observed during the dry and cold seasons and during early morning and evening periods of high activity. These results represent some of the first air quality monitoring measurements in Mombasa and highlight the need for more study.

Meteorology is a major driving force to poor urban air quality. This is due to its ability to influence the emissions, transport, formation, and deposition of air pollutants. In this study, the relationship between meteorological parameters including temperature, relative humidity, wind speed and direction and ambient air pollutants concentrations such as PM2.5 in the capital city of Ghana was carried out for a continuous period of 12 months from March 2020 to February 2021. Clear seasonality was observed for PM2.5, meteorological parameters and the air quality index. Maximum concentrations of PM2.5 were recorded in winter leading to poor air quality. Wind speed and relative humidity reversely correlated with the air pollutant while temperature showed a positive correlation with PM2.5. north-easterly winds led to highest concentrations during the winter season while south-westerly winds prevail over Accra in summer. The results from air quality index (AQI) indicated that severely poor air prevails during the winter period. These results justify the crucial role of meteorological parameters in air pollution formation with large variations in different seasons. These findings can be employed to enhance the understanding of processes that lead to air pollution and improve the accuracy of air quality forecast under different meteorological conditions.

Ambient fine particulate matter (PM2.5) concentrations in India frequently exceed 100 μg/m3 during fall and winter pollution episodes. We use the GEOS-Chem chemical transport model with the TwO-Moment Aerosol Sectional microphysics scheme with 15 size bins (TOMAS15) to assess PM2.5 composition and impacts on radiation and cloud condensation nuclei (CCN) during pollution episodes as compared to the seasonal (October-December) average. We conduct high resolution (0.25 degree x0.3125 degree) nested-domain simulations over India for short-duration, high-PM2.5 episodes in fall 2015 and 2017. The simulations capture the magnitude and spatial patterns of pollution episodes measured by surface monitors (r2PM2.5=0.69) although aerosol optical depth is underestimated. During the episodes, near-surface organic matter (OM), black carbon (BC), and secondary inorganic aerosol concentrations increase from seasonal averages by up to 36, 7, and 7 µg/m3, respectively. Episodic aerosol increases enhance cooling by lowering the top-of-atmosphere clear-sky direct radiative effect (DRETOA) during the 2015 episode (-6 W/m2), with a smaller impact during the 2017 episode (-1 W/m2). Differences in DRETOA reflect larger increases in scattering aerosols in the column during the 2015 episode (+17 mg/m2) than in 2017 (+13 mg/m2), while absorbing aerosol column enhancements are smaller (+3 mg/m2) in both years. Changes in shortwave radiation at the surface (SWsfc) are spatially similar to DRETOA and mostly negative during both episodes. CCN enhancements during these episodes occur across the western Indo-Gangetic Plain, coincident with higher PM2.5 concentrations. Changes in DRETOA, SWsfc, and CCN during high-PM2.5 episodes may have implications for crops, the hydrologic cycle, and surface temperature.

The contribution of individual aerosol species and greenhouse gases to precipitation changes during the South Asian summer monsoon is uncertain. Mechanisms driving responses to anthropogenic forcings needs further characterization. We use an atmosphere-only climate model to simulate the fast response of the summer monsoon to different anthropogenic aerosol types and to anthropogenic greenhouse gases. Without normalization, sulfate is the largest driver of precipitation change between 1850 and 2000, followed by black carbon and greenhouse gases. Normalized by radiative forcing, the most effective driver is black carbon. The precipitation and moisture budget responses to combinations of aerosol species perturbed together scale as a linear superposition of their individual responses. We use both a circulation-based and moisture budget-based argument to identify mechanisms of aerosol and greenhouse gas induced changes to precipitation, and find that in all cases the dynamic contribution is the dominant driver to precipitation change in the monsoon region.