Heatwaves Amplify Air Pollution Risks in Sub-Saharan Africa

Heatwaves Amplify Air Pollution Risks in Sub-Saharan Africa | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Heatwaves Amplify Air Pollution Risks in Sub-Saharan Africa Egide Kalisa, Andrew Sudmant This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6346347/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Despite mounting evidence that heatwaves aggravate urban air pollution, with substantial impacts on public health, comparatively little research has addressed Sub-Saharan African contexts. In this study, we focused on Kigali, Rwanda, to assess the relationship between extreme heat events and concentrations of fine particulate matter (PM₂.₅), nitrogen dioxide (NO₂), and ozone (O₃) from 2021 to 2024. Using low-cost sensors for dense spatiotemporal coverage, our analysis found that O₃ concentrations increased significantly during 6 heatwave events with peak values up to 40% higher during heatwaves than non-heatwave events in the afternoon. Heatwaves also resulted in spikes in PM 2.5 and NO 2 , however the diurnal and seasonal analyses showed that PM 2.5 and NO 2 dynamics were shaped more by local emissions sources than temperature alone. These results highlight the compound risks of heat and air pollution in sub-Saharan African cities, underscoring the importance of early-warning systems and robust urban policies that account for both heat and pollution. In addition, the atmospheric dynamics identified in this research differ from those observed in high-income countries, highlighting a critical need for more research exploring the intersection of heat and air pollution in Sub-Saharan Africa. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental sciences/Environmental impact Earth and environmental sciences/Climate sciences/Atmospheric science Earth and environmental sciences/Climate sciences/Climate change Heatwave Ozone Particulate matter Nitrogen dioxide Sub-Saharan Africa Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Heatwaves are occurring with increasing frequency and severity worldwide, primarily driven by anthropogenic climate change 1 , 2 . These extreme temperature events amplify air pollution through intensified photochemical reactions leading to higher ozone (O₃) formation, and by creating stagnant atmospheric conditions that trap pollutants near ground level 3 . Multiple large-scale studies confirm that heat and air pollution jointly produce greater health risks than either hazard alone. For instance, a recent global analysis of 620 cities demonstrated that high levels of particulate matter (PM) or ozone (O₃) significantly increased heat-related mortality 4 . Similar compound effects have been observed in California, where extreme heat and PM₂.₅ co-occurring on the same days nearly doubled the mortality risk compared to the sum of their individual impacts 5 . In Seoul, heatwaves substantially boosted O₃ and fine particulate pollution (PM 2.5 ), though the patterns for nitrogen dioxide (NO₂) and coarse particulate pollution (PM₁₀) varied depending on meteorological stagnation 6 . Urban centres in China have seen a steep rise in days combining extreme heat and high O₃, driving up total population exposure to “compound extremes” 7 . Power plants in India and China emit surges of sulphur dioxide (SO₂) and NO₂ during heatwaves due to soaring electricity demand in a feedback loop that worsens local smog right when populations are already heat-stressed 8 . Evidence of compound heat–pollution risks extend beyond mortality to encompass broader health outcomes. In California, co-exposure to extreme heat and elevated pollutants has been linked to preterm birth risks 9 , while in China, hypertension incidence among older adults rises disproportionately when heatwaves coincide with PM₂.₅ spikes, especially in neighbourhoods lacking green spaces 10 . Du et al reported that the risk of dying from cardiovascular and respiratory causes during concurrent heatwaves and elevated ozone was much higher than during heatwave-only days 11 . Together, these findings confirm that climate change is intensifying the overlap of extreme heat and polluted air, creating compound hazards that disproportionately threaten vulnerable populations including the elderly, pregnant individuals, and those with preexisting health conditions. Despite extensive research in Europe, North America, and parts of Asia, sub-Saharan Africa (SSA) remains underrepresented in academic literature 4 . When African contexts appear, they tend to be limited in geographic scope (often only a few South African cities) or treat temperature as a background variable rather than a catalyst that can directly drive air pollution levels 12 , 13 . This gap persists even as SSA undergoes rapid urbanization, grapples with growing industrial emissions, biomass burning, and natural dust intrusions and faces rising urban temperatures. For instance, a 2024 Nigerian heatwave dust event underscored how local factors (Saharan dust) can merge with extreme heat to cause severe air quality deterioration 14 . Similar conditions could manifest elsewhere on the continent, particularly in fast-growing cities with strained infrastructure, limited air quality monitoring, and rapidly increasing demand for energy 8 . Recent studies demonstrate that low-cost air quality sensors can help to bridge critical air quality monitoring gaps in sub-Saharan African cities by providing high-resolution spatiotemporal data 15 – 17 . For example, pilot sensor networks in Kinshasa and Brazzaville captured annual PM₂.₅ concentrations four to five times higher than WHO guidelines, and multiyear deployments in Lomé have shown significant seasonal spikes in pollution due to regional dust transport 17 . These examples highlight the potential of low-cost sensor networks to augment environmental data collection for climate-related air pollution research in data-scarce regions and the degree to which air quality risks in SSA may be an underappreciated risk to public health and well-being. Kigali, the capital of Rwanda, offers a case that could help to advance the understanding of compound urban heat and air pollution risks in a Sub-Saharan African context. As one of the fastest urbanizing cities in SSA 18 , Kigali faces emerging threats from extreme heat events, high background pollution from vehicle traffic and biomass burning, and limited capacity for comprehensive environmental monitoring. The core processes behind heatwave–pollution interactions, production of pollutants (especially ozone and secondary aerosols), reduced atmospheric mixing under stagnant high-pressure systems, and potential spikes in local emissions during hot periods are therefore found in Kigali 5 , 6 . This study has four main objectives. First, it quantifies changes in PM₂.₅, NO₂, and O₃ concentrations in Kigali during heatwave versus non-heatwave days. Second, it assesses the role of seasonality (dry vs wet) in modifying how extreme heat affects pollution levels. Third, it examines how humidity and air stagnation mediate pollution concentrations during heatwave episodes, shedding light on the meteorological underpinnings of these events in a tropical highland environment. Finally, it discusses policy implications, particularly around integrating heatwave alerts with air quality advisories to safeguard public health. Results 3.1. Overview of Data The average concentration of PM 2.5 , NO 2 , and O 3 from 2021 to 2024 was significantly higher in the dry and wet seasons (Table 1 ). The Wilcoxon–Mann–Whitney test showed that the annual means for NO 2 were significantly higher in the dry seasons than in the wet seasons. In contrast, the annual averages for PM 2.5 and O 3 were significantly higher in the wet than the dry seasons (Table 1 ). The diurnal analysis (Fig. 1 ) showed that PM 2.5 peaked during morning hours (6:00–9:00 AM) and evening rush hours (17:00–23:00 PM) and was higher during the wet seasons. O 3 peaked around midday (13:00 am -16:00 pm) in both dry and wet seasons, as expected due to photochemical activity ( Fig. 1 ). The concentration of NO 2 was significantly higher during dry seasons than wet seasons. The overall results indicated a distinct seasonal dynamic for different pollutants in Kigali. Table 1 Comparison of PM 2.5 , NO 2 , and O 3 mean concentrations between dry and wet seasons and during heatwave and non-heatwave days from 2021–2024. * The p-value indicates the presence of statistically significant differences among air pollutant concentrations based on the Wilcoxon test. Pollutants Dry Season [n = 71996] Wet Season [n = 58707] *p-value PM 2.5 [ µg/m 3 ] 37.7 ± 21.2 47.0 ± 23.2 < .001 NO 2 [µg/m 3 ] 21.3 ± 6.8 20.2 ± 6.5 < .001 O 3 [PPB] 21.7 ± 15.3 22.5 ± 10.5 < .001 Temp [°C] 20.6 ± 3.9 20.8 ± 3.9 < .001 RH [%] 66.0 ± 12.4 58.1 ± 13.8 < .001 Pollutants Non-Heatwave [n = 20912] Heatwave [n = 690] *p-value PM 2.5 [ µg/m 3 ] 41.4 ± 16.2 43.1 ± 13.9 < .001 NO 2 [µg/m 3 ] 15.1 ± 4.1 16.6 ± 5.2 < .001 O 3 [PPB] 21.5 ± 7.9 24.8 ± 8.3 < .001 3.2 Heatwave Identification We defined a heatwave as three or more consecutive days exceeding the average daily maximum temperature by 5°C, considering the period between 1961 and 1990 as the standard measuring period 19 . Based on this definition of a heatwave, six heatwaves were identified from 2021 to 2024, during which high intensities and long durations of elevated temperatures were observed (Table 2 ). Examining all six heatwaves, the highest maximum temperature was observed in March 2022 but corresponded to lower concentrations of PM 2.5 and NO 2 , suggesting more atmospheric dispersion. The most prolonged heatwaves, lasting five days, were observed in January 2022 and June 2023. Over the 4 years covered by the data in this study, the highest mean concentrations of PM 2.5 (55.6 (µg/m 3 ) and NO 2 (42.0 µg/m 3 ) coincided with heatwaves that involved five consecutive days with maximum temperatures above 32°C in 2022 and 2023, respectively (Table 2 ). While PM 2.5 and O 3 showed variations peaking during the longest heatwave peaks, NO 2 remained relatively stable during high-intensity and long-duration heatwaves. Table 2 Heatwave periods with corresponding annual maximum temperatures and concentrations of air pollutants (PM 2.5 , O 3 , NO 2 ) identified from 2021 to 2024. Year Month Period Max. Temp [°C] Mean PM 2.5 [µg/m 3 ] Mean NO 2 [µg/ m3 ] Mean O 3 [ppb] 2021 September 14th − 16th 33.20 39.75 16.84 24.65 2021 September 19th − 21st 32.78 40.35 16.05 27.46 2022 January 24th − 28th 32.65 55.59 17.24 26.42 2022 March 05th − 8th 33.47 30.28 15.89 21.70 2023 June 26th -30th 32.95 41.93 16.26 23.94 2023 July 9th -11th 32.36 40.04 16.41 23.56 Figure 2 shows the variation in the mean concentrations of air pollutants (PM 2.5 , O 3 , NO 2 ) and temperatures during heatwaves from 2021 to 2024. Generally, the O 3 and NO 2 concentrations on days with heatwaves were elevated; however, the PM 2.5 concentration did not always increase at the same time as temperatures, and peak concentrations were delayed (Fig. 2 ). In 2021, two short heatwaves of three consecutive days were identified with a moderate intensity of ~ 33ºC, and a minor peak in PM 2.5 was observed during or after heatwave days (Fig. 2 ). January 2022 was identified as the most prolonged and most intense heatwave, with five consecutive days where the temperature was consistently above 32ºC. A spike in PM 2.5 concentration (80µg/m 3 ) was observed before the heatwave while ozone gradually increased over the heatwave event (Fig. 2 ). Another heatwave of ~ 4 consecutive days was observed in March 2022 (Fig. 2 ) with the highest temperature reaching ~ 34ºC. This heatwave showed PM 2.5 and O 3 increases not coinciding with the heatwave but rising gradually after the heatwave. Another heatwave of 5 days was observed in June 2023 and showed levels of PM 2.5 and O 3 gradually increasing and peaking at the end of the heatwave (Fig. 2 ). O 3 levels showed a significant increase during and after heatwaves, with the highest concentration of 70 µg/m 3 observed after the heatwave. A moderate increase in NO 2 (~ 15 µg/m 3 ) was also observed during heatwaves. These results indicated that O 3 was the most heatwave-responsive pollutant, consistently peaking during and after heatwaves, while PM 2.5 showed variable timing, with peaks before, during and after heatwaves. NO 2 showed less variation with heatwaves, suggesting it is emissions-driven. 3.3 Characteristics of pollutants during heatwave events Table 1 compares average air pollutant concentrations between non-heatwave and heatwave periods. The results indicated that all pollutant concentrations were statistically significantly higher during heatwaves than during non-heatwave periods. Figure 3 shows the diurnal variation in PM 2.5 , NO 2 and O 3 during heatwaves and non-heatwaves. The concentration of PM 2.5 was consistently high during heatwaves, with peaks during the night and morning times (00–09:00 am) and also during the evening (20:00–23:00) but decreases in the middle of the day (10:00–16:00) during both heatwaves and non-heatwave events. O 3 showed the clearest response to heatwaves, increasing as the heatwaves increased, peaking significantly during the middle of the day (10:00–16:00) and remaining elevated even after the end of the heatwaves in the evening (17:00–20:00). NO 2 showed noticeable peaks in the middle of the day (~ 2:00 pm) during heatwaves and remained higher during the evenings (6:00–7:00 pm) on heatwave days, while in the morning the concentration dropped, due to increased photochemical conversion to O 3 . The findings showed that extended and intense heatwaves were strongly associated with increased O 3 concentrations during and after heatwaves. In contrast, PM 2.5 and NO 2 did not increase uniformly, showing delayed peaks during heatwaves, particularly in the evenings. Although longer heatwaves resulted in high pollution peaks of PM 2.5 and NO 2 , the diurnal and seasonal analyses showed that PM 2.5 and NO 2 dynamics were shaped more by local emissions sources than temperature alone during heatwaves, as indicated by existing literature on air pollution sources in Rwanda and Kigali 20 . We observed an unexpected peak of NO 2 coinciding with the temporal midday dip in O 3 during heatwaves, suggesting a unique interplay of nitric oxide (NO) titration and vertical mixing before rebounding sharply in the afternoon (Fig. 3 ). 3.4 Correlation analysis of air pollution and heatwaves Based on simple linear regression analysis, the relationship between PM 2.5 , NO 2 and O 3 and temperature during all six heatwaves was identified for the period 2021–2024 (Fig. 4 ). The results showed a strong correlation of O 3 with temperature (R 2 = 0.54, P < 0.001), suggesting that heatwaves may significantly contribute to ozone formation. Figure 4 C showed that the scatter was widely dispersed across the correction curve, suggesting that another precursor, such as sunlight intensity, may also influence O 3 formation. A moderate positive correlation was also observed for NO 2 and temperature during heatwaves (R 2 = 0.30, P < 0.001), suggesting that NO 2 tends to increase with temperature, although the correlation was not strong. Conversely, PM 2.5 showed a negative correlation (R 2 =-0.26, P < 0.001), suggesting that elevated temperature may enhance vertical mixing and dispersion of PM 2.5 , reducing ground concentrations. The yearly analysis from 2021 to 2024 of humidity and temperature (Fig. 4 A and 4 B) showed that both PM 2.5 and NO 2 decreased with increasing temperature and decreasing humidity, suggesting the influence of wet deposition, and indicating that these pollutants are more influenced by local emissions and dispersion effects. In contrast, O 3 showed an opposite trend to PM 2.5 and NO 2 with an increase in the dry months (June-September), a period known as the ozone months, which suggests a contribution of photochemical formation. These results suggest that humidity enhances particle removal via washout or wet deposition of PM 2.5 while lower humidity favours the accumulation of air pollutants. Discussion In the last 30 years, Rwanda has undergone rapid urbanization, population growth, and urban expansion. This rapid development has led to the creation of urban heat islands in larger cities, driven by high-density building and reductions in vegetation coverage, and increased air pollution due to the continued use of biomass for cooking and the growing number of vehicles for transport. Furthermore, Rwanda often experiences periods of low wind speeds and minimal rainfall, while its Eastern region is prone to El Niño events, which can bring both droughts and floods. Alongside these meteorological extremes, Rwanda has experienced an average temperature increase of 1.4°C in recent decades, with projections of reaching 2°C by 2030 21 . Heatwaves under these conditions can exacerbate health impacts of air pollution, notably increasing respiratory and cardiovascular stress among vulnerable populations. Similar issues have been documented worldwide, where both heatwaves and air pollution are linked to greater climate instability and higher mortality. Heatwaves can exacerbate the health impacts of air pollution, such as respiratory and cardiovascular stress, particularly in vulnerable populations 11 . Both heatwaves and air pollution are expected to lead to greater climate instability 22 , 23 . Extreme heat has been classified as global killer, associated with an increase in mortality reported in high-income countries such as the US 24 and the United Kingdom 25 . Sub-Saharan Africa experiences high levels of air pollution influenced by urbanization, traffic emissions, biomass burning, wildfires, and industrial activities, and it is one of the regions that is expected to be most affected by climate change 1 . Research in high-income countries has shown that air pollution and heatwaves compound, amplifying each other’s adverse effects 3 , 10 , 11 . Policies have been established to protect the general public from heatwaves and air pollution, with heat warnings and air quality alerts in place 26–28 . but these protections are not currently available in much of Africa, where many cities still lack sufficient long-term air quality monitoring and have limited funding to install infrastructure to obtain measurements related to pollution hotspots and spikes. Our analysis revealed that PM₂.₅ concentrations in both heatwave and non-heatwave periods often exceed WHO guidelines in Kigali, with levels occasionally reaching four times the recommended thresholds due to rapid urbanization, dust emissions, emissions from old diesel vehicles, and agricultural fires 20 ,29–32 . In contrast, O₃ and NO₂ generally fell within WHO limits but displayed notable spikes during heatwaves, particularly around the middle of the day for O₃. Surprisingly, PM₂.₅ peaked during the wet season, which deviates from global trends and points due to the role of Rwanda’s mountainous topography in retaining pollutants at ground level. High humidity further promotes aerosol formation and can interact with biomass burning emissions, potentially contributing to prolonged pollution episodes 31 , 33 . We also observed that extended heatwaves drive up O₃ and PM₂.₅ concentrations, whereas NO₂ levels were more strongly tied to local emissions than temperature alone. Correspondingly, daily “rush-hour” peaks in NO₂ and PM₂.₅ were detected in the morning and evening, and overnight emissions of PM₂.₅ often accumulated under low boundary-layer heights. Pearson correlation analyses underscored significant positive relationships between heatwaves and both O₃ and NO₂, while PM₂.₅ exhibited a negative correlation—indicating that PM₂.₅ can reach high levels even after heatwave conditions start to subside, reflecting the complex atmospheric dynamics in sub-Saharan Africa. These findings collectively emphasize the need for improved air quality monitoring networks and specialized early warning systems to address pollution spikes during and after prolonged heatwaves. These findings underscore the need for a comprehensive approach to managing heatwaves and air pollution in African cities, a need that will grow more critical with continued urban growth and global warming. While heatwave alerts and air quality interventions are well established in some high-income regions, such measures remain limited in many parts of sub-Saharan Africa. Coordinated efforts from meteorological agencies, health authorities, and local governments, and communication to the public via text-based advisories and public service announcements, could alert residents to imminent risks and recommend protective measures 34 . Lessons can be drawn from initiatives like the Freetown Heat Action Plan, which provides community-focused strategies and creates “cool zones” for vulnerable populations 35 . Implementing similar programs may help to reduce heat-related illnesses and limit exposure to pollution spikes during extreme weather events. Cost-effective and easily deployable sensors can strengthen air quality surveillance in regions with limited resources. Real-time data on pollution hotspots, particularly during and immediately after heatwaves, would allow authorities to target interventions, such as traffic restrictions or intensified enforcement of emissions regulations. Community-based sensors can also raise public awareness and foster local ownership of air quality and climate initiatives 36 . Addressing the dual challenges of urban heat and air pollution also requires policies that link climate resilience with emission reductions. In many African cities, soaring temperatures caused by the urban heat island effect are compounded by air pollution from traffic, industry, and inefficient energy use, trends that are intensified by rapid urbanization 37 .The most vulnerable communities, especially those in informal settlements, face disproportionate health risks due to inadequate housing, limited cooling options, and under-recognized exposure to extreme heat 38 . One promising avenue involves implementing nature-based solutions (NbS), such as urban tree planting, wetland restoration, and green roofing, which can simultaneously lower temperatures and filter pollutants 39 . By thoughtfully selecting tree species to avoid high biological VOC emissions, and integrating cleaner technologies (e.g., electric transit systems), cities can address the root causes of both heat and pollution 40, 41 . Moreover, stronger regulatory frameworks including tightened vehicle emission standards or industrial air quality controls can ensure that pollution-related hazards are minimized while also advancing climate goals 42 . Coordinating urban planning and policy across sectors yields the greatest benefits, as evidenced by studies showing that aggressive emission cuts avert thousands of premature deaths related to fine particulate matter and ozone exposure 43 . Cities such as Nairobi and Lagos have begun integrating climate resilience strategies into broader development plans, adopting clean energy and sustainable transport initiatives to achieve both climate and health co-benefits 44 . Fundamentally, aligning air quality management with climate adaptation and mitigation not only safeguards public health but also fosters more resilient, livable urban environments 20 ,32, 45 . Several limitations of this study are important to highlight. This study was constrained by a relatively short-term dataset covering only four years (2021–2024), collected from 12 sites in Kigali. Although these measurements provide valuable insights into heatwave–pollution interactions in a Sub-Saharan African context, the data may not fully capture long-term or interannual climate–pollution dynamics. Extending both the duration of monitoring and the number of sites would allow for more robust statistical analyses and a better understanding of how climatic trends over time influence air quality patterns in rapidly urbanizing regions. A second limitation involves the use of low-cost sensors. While these devices were calibrated against a Beta Attenuation Mass Monitor (BAM) station and demonstrated strong correlations (R > 0.60, p < 0.001), uncertainties remain due to inherent sensor variability and the limited availability of reference-grade monitors in the region. Additional reference sites, as well as standardized protocols for sensor calibration, could help refine data quality and improve comparability among different locations and time periods. In addition, the meteorological data considered in this study included only temperature and relative humidity. Other factors, such as wind speed, solar radiation, boundary-layer height, and atmospheric pressure, also play essential roles in determining pollutant dispersion and the photochemical processes that lead to elevated ozone. More generally, combining low-cost sensor data with other sources of meteorological, economic, social and land-use data may be a promising approach for future research. Finally, while this work illustrates how heatwaves can exacerbate air pollution, it does not include data on specific pollution sources or direct health outcomes. Understanding whether PM₂.₅, NO₂, or O₃ originate from traffic, biomass burning, industry, or dust storms, and how these pollutants affect cardiovascular or respiratory health, would strengthen the evidence base for targeted interventions. Studies that integrate robust source apportionment techniques alongside epidemiological data would enable more accurate assessments of risk and the design of tailored mitigation strategies, especially for vulnerable populations. Looking ahead, longer-term monitoring campaigns with broader geographic coverage, and enhanced meteorological data will help fill current gaps in knowledge. By linking air pollution data to health surveillance records, researchers can quantify the real-world impacts of compound hazards and guide resource allocation for public health interventions. This information, used to inform interdisciplinary efforts involving urban planners, policymakers, and public health practitioners are essential to address the growing challenges posed by rapid urbanization, climate variability, and limited monitoring infrastructure in Sub-Saharan African cities. Methods 5.1. Study Area & Data Sources Rwanda is a landlocked country in Sub-Saharan Africa, with a high population density (503 people per square kilometre) in 2022, which is the second highest in Africa after Mauritius. Rwanda has two main seasons (dry and wet) and experiences a tropical climate with air temperatures between 16ºC and 20ºC. Heatwaves occurring in Rwanda are associated with factors including climate, topography and socio-economic issues that influence the duration and intensity of heatwaves. Due to its thousands of hills and mountains and high altitude, in the past, Rwanda has experienced only rare heatwaves across the country. However, some valleys and urban basins in Kigali, the capital of Rwanda, can trap heat due to weather inversion. Further, the 1994 genocide against the Tutsi created widespread environmental degradation that has made Rwanda vulnerable to extreme climate weather events such as heatwaves. In the last 30 years, Rwanda has undergone rapid urbanization, population growth, and city expansion. This has created urban heat islands in larger cities as a result of high-density building and lack of vegetation. Rwanda also experiences periods of low wind speeds and little rainfall, especially during the wet season, and the Eastern region experiences El Niño events, causing droughts and floods in the East and Western provinces of Rwanda. Consequently, air quality during heatwaves in Rwanda may vary considerably, especially in Kigali City, the largest city in Rwanda, which suffers from air pollution episodes 20 , 31 ,32 . This study analyzed PM 2.5 , NO 2 , and O 3 concentrations obtained from 12 air quality stations in Kigali from May 2021 to December 2024 ( Fig. 5 ). These air quality stations are operated by the HELTH Research Group and the Rwanda Environmental Management Authority ( https://aq.rema.gov.rw/ ), and hourly averaged pollutant concentrations were analyzed along with meteorological data (daily minimum, mean and maximum temperatures, relative humidity) obtained from the Rwanda Meteorological Agency ( https://www.meteorwanda.gov.rw ). Air Pollution Measurements and Calibration Real-time PM 2.5 , NO 2 and O 3 data (measurements over 60 seconds) were collected at 12 sites (Fig. 1 ) in Kigali City using lower-cost, real-time, affordable multi-pollutant (RAMP) monitors from 2021–2024. The description and calibration of these RAMPs were published in our previous study in Rwanda 20 , 46 and detailed by Malings et al 47 . The RAMP system (Sensit Technology, USA) units were located 2–3 m above the ground, using passive electrochemical sensors (Alphasense, UK) to measure five gases and particles (carbon monoxide (CO), nitrogen dioxide (NO₂), ozone (O₃), particulate matter (PM 2.5 ) and meteorological parameters (temperature and relative humidity) 46 , 47 . These sensors collect raw signal data, which is then processed and averaged to produce 60-second air quality measurements. For local data verification in this study, we compared PM 2.5 data from 2021–2024 from RAMP to data for the same period obtained from a ground-monitored beta attenuation mass monitor (BAM) reference station operated by the US Embassy ( https://www.airnow.gov/ ) in Kigali from 2022–2024. This BAM station is 5–10 km from the RAM air quality station in Kigali. The correlation analysis showed that RAMP and BAM data correlated yearly (r2 > 0.6, P < 0.00). The correlation analysis showed that BAM-PM 2.5 data were significantly positively correlated with RAMP at 60%, suggesting some intra-urban variability associated with local activities, such as traffic, and the distance between stations with data that were not collocated. Due to data availability, we did not perform local verification of NO 2 and O 3 ; however, previous studies have conducted quality control and quality assurance using the generalized RAMP (gRAMP) calibration models developed in Pittsburgh 46 , 47 . Heatwave Definition No universally accepted heatwave definition exists, as climate norms vary widely between regions 23 , 48 . Rwanda is a tropical country in SSA where the average temperature ranges from 16°C to 20°C. Our heatwave definition was based on existing literature 3 . We defined a heatwave as three or more consecutive days exceeding the average daily maximum temperature by 5°C, with the daily average determined for the period between 1961 and 1990 19 . From 2021 to 2024, the daily maximum temperature was 25.28ºC for Kigali. We identified six heatwaves with maximum temperatures of 32.3°C − 33.5°C. We also defined non-heatwaves periods as days with temperatures below the 90th percentile threshold and not meeting our heatwave definition 23 . Our heatwave definition of three consecutive or more days with a temperature above 32°C may appear moderate compared to the definitions used in temperate regions. However, in Rwanda's high-altitude areas, including Kigali, the daily maximum temperature can be lower than 16–20°C. Thus, a sudden increase above 32°C represents almost 12–16°C above normal and may lead to thermal stress and health impacts. However, no epidemiological study has been conducted in Rwanda or Africa to determine the health outcomes of elevated temperatures. We therefore adopted the approach of using three or more conductive days with temperatures above 32°C, and we believe such thresholds are likely to induce heat-induced physiological stress, especially in vulnerable populations. Our definition aligns with low-resource settings, data gaps and observed weather extremes in the last decade, where the highest temperature ever recorded in Rwanda was 33°C in Kigali, according to the Rwanda Meteorological Agency. Our definition also aligns with several countries in East Africa, such as Uganda, and countries in Asia, such as Vietnam and Thailand, with similar baseline climates and topographies. For example, the neighboring country of Uganda has reported heat-related health warnings in Kampala city at 30–33°C 49 , while Vietnam 50 considers heatwaves to be temperatures of 32–35°C for 2 to 3 consecutive days. Therefore, heatwaves should be locally adapted based on population vulnerability, climate, topography, climate norms and health infrastructure. We believe that even a short period (3 days with a temperature above 32°C) may lead to emergency hospital visits, dehydration and stress in Rwanda, although these impacts remain unstudied. Declarations Author Contribution E.K and A.S contributed equally to the study's conceptualization, methodology, data analysis, manuscript review, and editing. Acknowledgement We acknowledge the air quality data that the Rwanda Environmental Management Authority provided. Data Availability PM 2.5 , NO 2 , and O 3 data from 2021–2024, along with meteorological parameters, are available at https://aq.rema.gov.rw/ and https://meteorwanda.gov.rw/ . All the data are reproduced and adapted from the table provided in this study and literature. All data are available on request from the authors. 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Performance characterization of low-cost air quality sensors for off-grid deployment in rural Malawi. Atmospheric Meas. Tech. 15 , 3353–3376 (2022). Raheja, G. et al. A Network of Field-Calibrated Low-Cost Sensor Measurements of PM 2.5 in Lomé, Togo, Over One to Two Years. ACS Earth Space Chem. 6 , 1011–1021 (2022). World Bank. Concept Project Information Document (PID) - Rwanda Urban Mobility Project. http://documents.worldbank.org/curated/en/099350412232221350 (2022). Perkins, S. E. & Alexander, L. V. On the Measurement of Heat Waves. J. Clim. 26 , 4500–4517 (2013). Kalisa, E., Sudmant, A., Ruberambuga, R. & Bower, J. Natural experiments in urban air quality: lessons from car-free days and COVID-19 lockdowns in Kigali, Rwanda. Cities Health 1–12 (2025) https://doi.org/10.1080/23748834.2025.2468017. United Nations Development Programme. Updated Nationally Determined Contribution (NDC) Mitigation and Adaptation Priorities for Rwanda. https://climatepromise.undp.org/sites/default/files/research_report_document/undp-ndcsp-rwanda-ndc2-2020.pdf (2020). Grigorieva, E. & Lukyanets, A. Combined Effect of Hot Weather and Outdoor Air Pollution on Respiratory Health: Literature Review. Atmosphere 12 , 790 (2021). Li, C., Wei, W., Chan, P. W. & Huang, J. Heatwaves in Hong Kong and their influence on pollution and extreme precipitation. Atmospheric Res. 315 , 107845 (2025). Anderson, G. B. & Bell, M. L. Heat Waves in the United States: Mortality Risk during Heat Waves and Effect Modification by Heat Wave Characteristics in 43 U.S. Communities. Environ. Health Perspect. 119 , 210–218 (2011). Sahani, J., Kumar, P., Debele, S. & Emmanuel, R. Heat risk of mortality in two different regions of the United Kingdom. Sustain. Cities Soc. 80 , 103758 (2022). Goverment of Canada. Heat warning and information system harmonization. (2016). https://www.canada.ca/en/environment-climate-change/news/2016/05/heat-warning-and-information-system-harmonization.html US EPA. New York City Adapts To Deal with Projected Increase of Heat Waves. (2025). https://www.epa.gov/arc-x/new-york-city-adapts-deal-projected-increase-heat-waves#:~:text=In%20order%20to%20adapt%20to,future%20projected%20extreme%20heat%20events.28. NOAA. National Integrated Heat Health Information System (NIHHIS). (2025). https://www.heat.gov/ Irankunda, E. et al. The comparison between in-situ monitored data and modelled results of nitrogen dioxide (NO2): case-study, road networks of Kigali city, Rwanda. Heliyon 8 , e12390 (2022). Kalisa, E., Kuuire, V. & Adams, M. Children’s exposure to indoor and outdoor black carbon and particulate matter air pollution at school in Rwanda, Central-East Africa. Environ. Adv. 11 , 100334 (2023). Kalisa, E. et al. Characterization and Risk Assessment of Atmospheric PM₂. ₅ and PM₁₀ Particulate-Bound PAHs and NPAHs in Rwanda, Central-East Africa. Environ. Sci. Technol. 52 , 12179-12187 (2018). Kalisa, E. & Adams, M. Population-scale COVID-19 curfew effects on urban black carbon concentrations and sources in Kigali, Rwanda. Urban Clim. 46 , 101312 (2022). DeWitt, H. L. et al. Seasonal and diurnal variability in O3, black carbon, and CO measured at the Rwanda Climate Observatory. Atmospheric Chem. Phys. 19 , 2063–2078 (2019). Wright, C. Y. et al. Climate Change and Human Health in Africa in Relation to Opportunities to Strengthen Mitigating Potential and Adaptive Capacity: Strategies to Inform an African “Brains Trust”. Ann. Glob. Health 90 , 7 (2024). Freetown City Council. Trees, women and data: Early lessons from Freetown’s Heat Action Plan. (2023). https://www.climateresilience.org/freetown-heat-action-plan-lessons Manshur, T. et al. A citizen science approach for air quality monitoring in a Kenyan informal development. City Environ. Interact. 19 , 100105 (2023). Li, X., Stringer, L. C. & Dallimer, M. The Impacts of Urbanisation and Climate Change on the Urban Thermal Environment in Africa. Climate 10 , 164 (2022). Pasquini, L., Van Aardenne, L., Godsmark, C. N., Lee, J. & Jack, C. Emerging climate change-related public health challenges in Africa: A case study of the heat-health vulnerability of informal settlement residents in Dar es Salaam, Tanzania. Sci. Total Environ. 747 , 141355 (2020). Kiribou, R. et al. Urban climate resilience in Africa: a review of nature-based solution in African cities’ adaptation plans. Discov. Sustain. 5 , 94 (2024). Anbazu, J. & Antwi, N. S. Nexus Between Heat and Air Pollution in Urban Areas and the Role of Resilience Planning in Mitigating These Threats. Adv. Environ. Eng. Res. 04 , 047 (2023). Mbandi, A. M. et al. Assessment of the impact of road transport policies on air pollution and greenhouse gas emissions in Kenya. Energy Strategy Rev. 49 , 101120 (2023). Okello, G. et al. Air quality management strategies in Africa: A scoping review of the content, context, co-benefits and unintended consequences. Environ. Int. 171 , 107709 (2023). Wells, C. D., Kasoar, M., Ezzati, M. & Voulgarakis, A. Significant human health co-benefits of mitigating African emissions. Atmospheric Chem. Phys. 24 , 1025–1039 (2024). Akomolafe, B., Clarke, A. & Ayambire, R. Climate Change Mitigation Perspectives from Sub-Saharan Africa: The Technical Pathways to Deep Decarbonization at the City Level. Atmosphere 15 , 1190 (2024). Atuyambe, L. M. et al. The Health Impacts of Air Pollution in the Context of Changing Climate in Africa: A Narrative Review with Recommendations for Action. Ann. Glob. Health 90 , 76 (2024). Subramanian, R. et al. Air pollution in Kigali, Rwanda: spatial and temporal variability, source contributions, and the impact of car-free Sundays. Clean Air J. 30 , 1–15 (2020). Malings, C. et al. Development of a general calibration model and long-term performance evaluation of low-cost sensors for air pollutant gas monitoring. Atmospheric Meas. Tech. 12 , 903–920 (2019). Meehl, G. A. et al. An Introduction to Trends in Extreme Weather and Climate Events: Observations, Socioeconomic Impacts, Terrestrial Ecological Impacts, and Model Projections * . Bull. Am. Meteorol. Soc. 81 , 413–416 (2000). Twinomuhangi, R. et al. Perceptions and vulnerability to climate change among the urban poor in Kampala City, Uganda. Reg. Environ. Change 21 , 39 (2021). Ha, P.-T. et al. Heatwaves in Vietnam: Characteristics and relationship with large-scale climate drivers. Int. J. Climatol. 44 , 48725-4740 (2024). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 21 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 27 May, 2025 Reviews received at journal 26 May, 2025 Reviews received at journal 11 May, 2025 Reviewers agreed at journal 05 May, 2025 Reviewers agreed at journal 02 May, 2025 Reviews received at journal 28 Apr, 2025 Reviewers agreed at journal 16 Apr, 2025 Reviewers invited by journal 09 Apr, 2025 Editor assigned by journal 08 Apr, 2025 Editor invited by journal 07 Apr, 2025 Submission checks completed at journal 07 Apr, 2025 First submitted to journal 31 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6346347","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":436441152,"identity":"c7acf2e7-cc6a-4736-9c5e-143ff48ff002","order_by":0,"name":"Egide Kalisa","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYDACHgbGAwkMDHL8pGhhAGkxlmwgSQuQStxwgFgd/DyHHxx48MeOcfONHMMHDDV2hLVI9rYZHEhsS2Y2u5FjbMBwLJmwFoPzDEAtDcxsZjdyt0kwNjAT1mJ/nv3DgYQ/9TzGM8Ba6omwhbfH4EAC22EJAwmwlsOEtUicOVMA9MtxA4kz7z8bJBw7TlgLf0/6xoc//lTX97enJT74UFNNWAsqSCBVwygYBaNgFIwC7AAAMSI7+glb9BIAAAAASUVORK5CYII=","orcid":"","institution":"Western University","correspondingAuthor":true,"prefix":"","firstName":"Egide","middleName":"","lastName":"Kalisa","suffix":""},{"id":436441154,"identity":"3f37c7d1-dc24-4ec4-af81-9c78f8159423","order_by":1,"name":"Andrew Sudmant","email":"","orcid":"","institution":"Edinburgh University","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"Sudmant","suffix":""}],"badges":[],"createdAt":"2025-03-31 15:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6346347/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6346347/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-12210-4","type":"published","date":"2025-07-21T15:57:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79778891,"identity":"3ddb4c88-6939-4f1e-ba34-dfe52a7f22ac","added_by":"auto","created_at":"2025-04-02 14:38:55","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":380159,"visible":true,"origin":"","legend":"\u003cp\u003eHourly variation of (A) PM\u003csub\u003e2.5\u003c/sub\u003e, (B) NO\u003csub\u003e2\u003c/sub\u003e, and (C) O\u003csub\u003e3\u003c/sub\u003e during both dry (red) and wet seasons (green).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6346347/v1/160796bf4dcc8f80e3e037c0.png"},{"id":79777721,"identity":"7b108c3b-77e5-4f64-a073-d3a326566954","added_by":"auto","created_at":"2025-04-02 14:30:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":618181,"visible":true,"origin":"","legend":"\u003cp\u003eConcentrations of air pollutants (PM\u003csub\u003e2.5\u003c/sub\u003e, NO\u003csub\u003e2 \u003c/sub\u003eand O\u003csub\u003e3\u003c/sub\u003e) and temperatures during heatwaves identified in Table 2 from 2021-2024 in Kigali, Rwanda\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6346347/v1/2e239262477e03741f2c5576.png"},{"id":79777724,"identity":"726463a7-ceef-43cf-aa4f-d27942688e9e","added_by":"auto","created_at":"2025-04-02 14:30:55","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":400234,"visible":true,"origin":"","legend":"\u003cp\u003eHourly variation of PM\u003csub\u003e2.5\u003c/sub\u003e, O\u003csub\u003e3\u003c/sub\u003e, and NO\u003csub\u003e2\u003c/sub\u003e during heatwaves (Yes: Red color) and non-heatwaves (No: Green color).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6346347/v1/247db601ff7ecbe9f7ede885.png"},{"id":79777725,"identity":"e7981f7d-11ab-448b-a960-c09c3ee36085","added_by":"auto","created_at":"2025-04-02 14:30:55","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":437048,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of air pollutants (PM\u003csub\u003e10\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e) during heatwaves identified in 2021, 2022 and 2023\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6346347/v1/d06cccebed07526f0c0c5979.png"},{"id":79777728,"identity":"31ae2f69-a1dd-4c28-86cf-6261079f86ef","added_by":"auto","created_at":"2025-04-02 14:30:55","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2493906,"visible":true,"origin":"","legend":"\u003cp\u003eMap of Africa (A) showing the geographic location of Rwanda and the city of Kigali (B). Map (C) shows the locations of the air quality monitoring stations (blue circles).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6346347/v1/aafaf5a001326e2a7a5f68c9.png"},{"id":87757583,"identity":"1ad17034-a19f-4b55-8cdc-13b74686502c","added_by":"auto","created_at":"2025-07-28 16:11:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4942204,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6346347/v1/9c1e8762-97fc-4720-aebb-673f73c8b893.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Heatwaves Amplify Air Pollution Risks in Sub-Saharan Africa","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHeatwaves are occurring with increasing frequency and severity worldwide, primarily driven by anthropogenic climate change \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. These extreme temperature events amplify air pollution through intensified photochemical reactions leading to higher ozone (O₃) formation, and by creating stagnant atmospheric conditions that trap pollutants near ground level\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Multiple large-scale studies confirm that heat and air pollution jointly produce greater health risks than either hazard alone. For instance, a recent global analysis of 620 cities demonstrated that high levels of particulate matter (PM) or ozone (O₃) significantly increased heat-related mortality\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSimilar compound effects have been observed in California, where extreme heat and PM₂.₅ co-occurring on the same days nearly doubled the mortality risk compared to the sum of their individual impacts \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. In Seoul, heatwaves substantially boosted O₃ and fine particulate pollution (PM\u003csub\u003e2.5\u003c/sub\u003e), though the patterns for nitrogen dioxide (NO₂) and coarse particulate pollution (PM₁₀) varied depending on meteorological stagnation\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Urban centres in China have seen a steep rise in days combining extreme heat and high O₃, driving up total population exposure to \u0026ldquo;compound extremes\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Power plants in India and China emit surges of sulphur dioxide (SO₂) and NO₂ during heatwaves due to soaring electricity demand in a feedback loop that worsens local smog right when populations are already heat-stressed\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eEvidence of compound heat\u0026ndash;pollution risks extend beyond mortality to encompass broader health outcomes. In California, co-exposure to extreme heat and elevated pollutants has been linked to preterm birth risks\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, while in China, hypertension incidence among older adults rises disproportionately when heatwaves coincide with PM₂.₅ spikes, especially in neighbourhoods lacking green spaces \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Du et al reported that the risk of dying from cardiovascular and respiratory causes during concurrent heatwaves and elevated ozone was much higher than during heatwave-only days\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Together, these findings confirm that climate change is intensifying the overlap of extreme heat and polluted air, creating compound hazards that disproportionately threaten vulnerable populations including the elderly, pregnant individuals, and those with preexisting health conditions.\u003c/p\u003e \u003cp\u003eDespite extensive research in Europe, North America, and parts of Asia, sub-Saharan Africa (SSA) remains underrepresented in academic literature\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. When African contexts appear, they tend to be limited in geographic scope (often only a few South African cities) or treat temperature as a background variable rather than a catalyst that can directly drive air pollution levels\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. This gap persists even as SSA undergoes rapid urbanization, grapples with growing industrial emissions, biomass burning, and natural dust intrusions and faces rising urban temperatures. For instance, a 2024 Nigerian heatwave dust event underscored how local factors (Saharan dust) can merge with extreme heat to cause severe air quality deterioration\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Similar conditions could manifest elsewhere on the continent, particularly in fast-growing cities with strained infrastructure, limited air quality monitoring, and rapidly increasing demand for energy\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecent studies demonstrate that low-cost air quality sensors can help to bridge critical air quality monitoring gaps in sub-Saharan African cities by providing high-resolution spatiotemporal data \u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. For example, pilot sensor networks in Kinshasa and Brazzaville captured annual PM₂.₅ concentrations four to five times higher than WHO guidelines, and multiyear deployments in Lom\u0026eacute; have shown significant seasonal spikes in pollution due to regional dust transport\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. These examples highlight the potential of low-cost sensor networks to augment environmental data collection for climate-related air pollution research in data-scarce regions and the degree to which air quality risks in SSA may be an underappreciated risk to public health and well-being.\u003c/p\u003e \u003cp\u003eKigali, the capital of Rwanda, offers a case that could help to advance the understanding of compound urban heat and air pollution risks in a Sub-Saharan African context. As one of the fastest urbanizing cities in SSA\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, Kigali faces emerging threats from extreme heat events, high background pollution from vehicle traffic and biomass burning, and limited capacity for comprehensive environmental monitoring. The core processes behind heatwave\u0026ndash;pollution interactions, production of pollutants (especially ozone and secondary aerosols), reduced atmospheric mixing under stagnant high-pressure systems, and potential spikes in local emissions during hot periods are therefore found in Kigali\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study has four main objectives. First, it quantifies changes in PM₂.₅, NO₂, and O₃ concentrations in Kigali during heatwave versus non-heatwave days. Second, it assesses the role of seasonality (dry vs wet) in modifying how extreme heat affects pollution levels. Third, it examines how humidity and air stagnation mediate pollution concentrations during heatwave episodes, shedding light on the meteorological underpinnings of these events in a tropical highland environment. Finally, it discusses policy implications, particularly around integrating heatwave alerts with air quality advisories to safeguard public health.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Overview of Data\u003c/h2\u003e \u003cp\u003eThe average concentration of PM\u003csub\u003e2.5\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e from 2021 to 2024 was significantly higher in the dry and wet seasons (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The Wilcoxon\u0026ndash;Mann\u0026ndash;Whitney test showed that the annual means for NO\u003csub\u003e2\u003c/sub\u003e were significantly higher in the dry seasons than in the wet seasons. In contrast, the annual averages for PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e were significantly higher in the wet than the dry seasons (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The diurnal analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) showed that PM\u003csub\u003e2.5\u003c/sub\u003e peaked during morning hours (6:00\u0026ndash;9:00 AM) and evening rush hours (17:00\u0026ndash;23:00 PM) and was higher during the wet seasons. O\u003csub\u003e3\u003c/sub\u003e peaked around midday (13:00 am -16:00 pm) in both dry and wet seasons, as expected due to photochemical activity \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e The concentration of NO\u003csub\u003e2\u003c/sub\u003e was significantly higher during dry seasons than wet seasons. The overall results indicated a distinct seasonal dynamic for different pollutants in Kigali.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of PM\u003csub\u003e2.5\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e mean concentrations between dry and wet seasons and during heatwave and non-heatwave days from 2021\u0026ndash;2024. * The p-value indicates the presence of statistically significant differences among air pollutant concentrations based on the Wilcoxon test.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePollutants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDry Season\u003c/p\u003e \u003cp\u003e[n\u0026thinsp;=\u0026thinsp;71996]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWet Season\u003c/p\u003e \u003cp\u003e[n\u0026thinsp;=\u0026thinsp;58707]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e*p-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM\u003csub\u003e2.5 [\u003c/sub\u003e\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.7\u0026thinsp;\u0026plusmn;\u0026thinsp;21.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.0\u0026thinsp;\u0026plusmn;\u0026thinsp;23.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e [\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e [PPB]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.7\u0026thinsp;\u0026plusmn;\u0026thinsp;15.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemp [\u0026deg;C]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRH [%]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e66.0\u0026thinsp;\u0026plusmn;\u0026thinsp;12.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.1\u0026thinsp;\u0026plusmn;\u0026thinsp;13.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePollutants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eNon-Heatwave\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e[n\u0026thinsp;=\u0026thinsp;20912]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eHeatwave\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003e[n\u0026thinsp;=\u0026thinsp;690]\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e*p-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM\u003csub\u003e2.5 [\u003c/sub\u003e\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.4\u0026thinsp;\u0026plusmn;\u0026thinsp;16.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.1\u0026thinsp;\u0026plusmn;\u0026thinsp;13.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e [\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.1\u0026thinsp;\u0026plusmn;\u0026thinsp;4.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e [PPB]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Heatwave Identification\u003c/h2\u003e \u003cp\u003eWe defined a heatwave as three or more consecutive days exceeding the average daily maximum temperature by 5\u0026deg;C, considering the period between 1961 and 1990 as the standard measuring period\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Based on this definition of a heatwave, six heatwaves were identified from 2021 to 2024, during which high intensities and long durations of elevated temperatures were observed (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Examining all six heatwaves, the highest maximum temperature was observed in March 2022 but corresponded to lower concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e, suggesting more atmospheric dispersion. The most prolonged heatwaves, lasting five days, were observed in January 2022 and June 2023. Over the 4 years covered by the data in this study, the highest mean concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e (55.6 (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) and NO\u003csub\u003e2\u003c/sub\u003e (42.0 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) coincided with heatwaves that involved five consecutive days with maximum temperatures above 32\u0026deg;C in 2022 and 2023, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). While PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e showed variations peaking during the longest heatwave peaks, NO\u003csub\u003e2\u003c/sub\u003e remained relatively stable during high-intensity and long-duration heatwaves.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHeatwave periods with corresponding annual maximum temperatures and concentrations of air pollutants (PM\u003csub\u003e2.5\u003c/sub\u003e, O\u003csub\u003e3\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e) identified from 2021 to 2024.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePeriod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMax. Temp\u003c/p\u003e \u003cp\u003e[\u0026deg;C]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean PM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e[\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMean NO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003cp\u003e[\u0026micro;g/\u003csup\u003em3\u003c/sup\u003e]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean O\u003csub\u003e3\u003c/sub\u003e [ppb]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeptember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14th \u0026minus;\u0026thinsp;16th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeptember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19th \u0026minus;\u0026thinsp;21st\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJanuary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24th \u0026minus;\u0026thinsp;28th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e05th \u0026minus;\u0026thinsp;8th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJune\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26th -30th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJuly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9th -11th\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e32.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the variation in the mean concentrations of air pollutants (PM\u003csub\u003e2.5\u003c/sub\u003e, O\u003csub\u003e3\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e) and temperatures during heatwaves from 2021 to 2024. Generally, the O\u003csub\u003e3\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e concentrations on days with heatwaves were elevated; however, the PM\u003csub\u003e2.5\u003c/sub\u003e concentration did not always increase at the same time as temperatures, and peak concentrations were delayed (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In 2021, two short heatwaves of three consecutive days were identified with a moderate intensity of ~\u0026thinsp;33\u0026ordm;C, and a minor peak in PM\u003csub\u003e2.5\u003c/sub\u003e was observed during or after heatwave days (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). January 2022 was identified as the most prolonged and most intense heatwave, with five consecutive days where the temperature was consistently above 32\u0026ordm;C. A spike in PM\u003csub\u003e2.5\u003c/sub\u003e concentration (80\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) was observed before the heatwave while ozone gradually increased over the heatwave event (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Another heatwave of ~\u0026thinsp;4 consecutive days was observed in March 2022 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) with the highest temperature reaching\u0026thinsp;~\u0026thinsp;34\u0026ordm;C. This heatwave showed PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e increases not coinciding with the heatwave but rising gradually after the heatwave. Another heatwave of 5 days was observed in June 2023 and showed levels of PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e gradually increasing and peaking at the end of the heatwave (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). O\u003csub\u003e3\u003c/sub\u003e levels showed a significant increase during and after heatwaves, with the highest concentration of 70 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e observed after the heatwave. A moderate increase in NO\u003csub\u003e2\u003c/sub\u003e (~\u0026thinsp;15 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) was also observed during heatwaves. These results indicated that O\u003csub\u003e3\u003c/sub\u003e was the most heatwave-responsive pollutant, consistently peaking during and after heatwaves, while PM\u003csub\u003e2.5\u003c/sub\u003e showed variable timing, with peaks before, during and after heatwaves. NO\u003csub\u003e2\u003c/sub\u003e showed less variation with heatwaves, suggesting it is emissions-driven.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Characteristics of pollutants during heatwave events\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e compares average air pollutant concentrations between non-heatwave and heatwave periods. The results indicated that all pollutant concentrations were statistically significantly higher during heatwaves than during non-heatwave periods. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the diurnal variation in PM\u003csub\u003e2.5\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e during heatwaves and non-heatwaves. The concentration of PM\u003csub\u003e2.5\u003c/sub\u003e was consistently high during heatwaves, with peaks during the night and morning times (00\u0026ndash;09:00 am) and also during the evening (20:00\u0026ndash;23:00) but decreases in the middle of the day (10:00\u0026ndash;16:00) during both heatwaves and non-heatwave events. O\u003csub\u003e3\u003c/sub\u003e showed the clearest response to heatwaves, increasing as the heatwaves increased, peaking significantly during the middle of the day (10:00\u0026ndash;16:00) and remaining elevated even after the end of the heatwaves in the evening (17:00\u0026ndash;20:00). NO\u003csub\u003e2\u003c/sub\u003e showed noticeable peaks in the middle of the day (~\u0026thinsp;2:00 pm) during heatwaves and remained higher during the evenings (6:00\u0026ndash;7:00 pm) on heatwave days, while in the morning the concentration dropped, due to increased photochemical conversion to O\u003csub\u003e3\u003c/sub\u003e. The findings showed that extended and intense heatwaves were strongly associated with increased O\u003csub\u003e3\u003c/sub\u003e concentrations during and after heatwaves. In contrast, PM\u003csub\u003e2.5\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e did not increase uniformly, showing delayed peaks during heatwaves, particularly in the evenings. Although longer heatwaves resulted in high pollution peaks of PM\u003csub\u003e2.5\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e, the diurnal and seasonal analyses showed that PM\u003csub\u003e2.5\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e dynamics were shaped more by local emissions sources than temperature alone during heatwaves, as indicated by existing literature on air pollution sources in Rwanda and Kigali\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. We observed an unexpected peak of NO\u003csub\u003e2\u003c/sub\u003e coinciding with the temporal midday dip in O\u003csub\u003e3\u003c/sub\u003e during heatwaves, suggesting a unique interplay of nitric oxide (NO) titration and vertical mixing before rebounding sharply in the afternoon (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Correlation analysis of air pollution and heatwaves\u003c/h2\u003e \u003cp\u003eBased on simple linear regression analysis, the relationship between PM\u003csub\u003e2.5\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e and temperature during all six heatwaves was identified for the period 2021\u0026ndash;2024 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The results showed a strong correlation of O\u003csub\u003e3\u003c/sub\u003e with temperature (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.54, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that heatwaves may significantly contribute to ozone formation. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC showed that the scatter was widely dispersed across the correction curve, suggesting that another precursor, such as sunlight intensity, may also influence O\u003csub\u003e3\u003c/sub\u003e formation. A moderate positive correlation was also observed for NO\u003csub\u003e2\u003c/sub\u003e and temperature during heatwaves (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.30, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that NO\u003csub\u003e2\u003c/sub\u003e tends to increase with temperature, although the correlation was not strong. Conversely, PM\u003csub\u003e2.5\u003c/sub\u003e showed a negative correlation (R\u003csup\u003e2\u003c/sup\u003e=-0.26, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that elevated temperature may enhance vertical mixing and dispersion of PM\u003csub\u003e2.5\u003c/sub\u003e, reducing ground concentrations. The yearly analysis from 2021 to 2024 of humidity and temperature (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB) showed that both PM\u003csub\u003e2.5\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e decreased with increasing temperature and decreasing humidity, suggesting the influence of wet deposition, and indicating that these pollutants are more influenced by local emissions and dispersion effects. In contrast, O\u003csub\u003e3\u003c/sub\u003e showed an opposite trend to PM\u003csub\u003e2.5\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e with an increase in the dry months (June-September), a period known as the ozone months, which suggests a contribution of photochemical formation. These results suggest that humidity enhances particle removal via washout or wet deposition of PM\u003csub\u003e2.5\u003c/sub\u003e while lower humidity favours the accumulation of air pollutants.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn the last 30 years, Rwanda has undergone rapid urbanization, population growth, and urban expansion. This rapid development has led to the creation of urban heat islands in larger cities, driven by high-density building and reductions in vegetation coverage, and increased air pollution due to the continued use of biomass for cooking and the growing number of vehicles for transport. Furthermore, Rwanda often experiences periods of low wind speeds and minimal rainfall, while its Eastern region is prone to El Ni\u0026ntilde;o events, which can bring both droughts and floods.\u003c/p\u003e \u003cp\u003eAlongside these meteorological extremes, Rwanda has experienced an average temperature increase of 1.4\u0026deg;C in recent decades, with projections of reaching 2\u0026deg;C by 2030 \u003csup\u003e21\u003c/sup\u003e. Heatwaves under these conditions can exacerbate health impacts of air pollution, notably increasing respiratory and cardiovascular stress among vulnerable populations. Similar issues have been documented worldwide, where both heatwaves and air pollution are linked to greater climate instability and higher mortality.\u003c/p\u003e \u003cp\u003eHeatwaves can exacerbate the health impacts of air pollution, such as respiratory and cardiovascular stress, particularly in vulnerable populations\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Both heatwaves and air pollution are expected to lead to greater climate instability\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Extreme heat has been classified as global killer, associated with an increase in mortality reported in high-income countries such as the US\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e and the United Kingdom \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSub-Saharan Africa experiences high levels of air pollution influenced by urbanization, traffic emissions, biomass burning, wildfires, and industrial activities, and it is one of the regions that is expected to be most affected by climate change\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Research in high-income countries has shown that air pollution and heatwaves compound, amplifying each other\u0026rsquo;s adverse effects \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Policies have been established to protect the general public from heatwaves and air pollution, with heat warnings and air quality alerts in place\u003csup\u003e26\u0026ndash;28\u003c/sup\u003e. but these protections are not currently available in much of Africa, where many cities still lack sufficient long-term air quality monitoring and have limited funding to install infrastructure to obtain measurements related to pollution hotspots and spikes.\u003c/p\u003e \u003cp\u003eOur analysis revealed that PM₂.₅ concentrations in both heatwave and non-heatwave periods often exceed WHO guidelines in Kigali, with levels occasionally reaching four times the recommended thresholds due to rapid urbanization, dust emissions, emissions from old diesel vehicles, and agricultural fires \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,29\u0026ndash;32\u003c/sup\u003e. In contrast, O₃ and NO₂ generally fell within WHO limits but displayed notable spikes during heatwaves, particularly around the middle of the day for O₃. Surprisingly, PM₂.₅ peaked during the wet season, which deviates from global trends and points due to the role of Rwanda\u0026rsquo;s mountainous topography in retaining pollutants at ground level. High humidity further promotes aerosol formation and can interact with biomass burning emissions, potentially contributing to prolonged pollution episodes\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e31\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWe also observed that extended heatwaves drive up O₃ and PM₂.₅ concentrations, whereas NO₂ levels were more strongly tied to local emissions than temperature alone. Correspondingly, daily \u0026ldquo;rush-hour\u0026rdquo; peaks in NO₂ and PM₂.₅ were detected in the morning and evening, and overnight emissions of PM₂.₅ often accumulated under low boundary-layer heights. Pearson correlation analyses underscored significant positive relationships between heatwaves and both O₃ and NO₂, while PM₂.₅ exhibited a negative correlation\u0026mdash;indicating that PM₂.₅ can reach high levels even after heatwave conditions start to subside, reflecting the complex atmospheric dynamics in sub-Saharan Africa. These findings collectively emphasize the need for improved air quality monitoring networks and specialized early warning systems to address pollution spikes during and after prolonged heatwaves.\u003c/p\u003e \u003cp\u003eThese findings underscore the need for a comprehensive approach to managing heatwaves and air pollution in African cities, a need that will grow more critical with continued urban growth and global warming. While heatwave alerts and air quality interventions are well established in some high-income regions, such measures remain limited in many parts of sub-Saharan Africa. Coordinated efforts from meteorological agencies, health authorities, and local governments, and communication to the public via text-based advisories and public service announcements, could alert residents to imminent risks and recommend protective measures\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Lessons can be drawn from initiatives like the Freetown Heat Action Plan, which provides community-focused strategies and creates \u0026ldquo;cool zones\u0026rdquo; for vulnerable populations\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Implementing similar programs may help to reduce heat-related illnesses and limit exposure to pollution spikes during extreme weather events.\u003c/p\u003e \u003cp\u003eCost-effective and easily deployable sensors can strengthen air quality surveillance in regions with limited resources. Real-time data on pollution hotspots, particularly during and immediately after heatwaves, would allow authorities to target interventions, such as traffic restrictions or intensified enforcement of emissions regulations. Community-based sensors can also raise public awareness and foster local ownership of air quality and climate initiatives\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAddressing the dual challenges of urban heat and air pollution also requires policies that link climate resilience with emission reductions. In many African cities, soaring temperatures caused by the urban heat island effect are compounded by air pollution from traffic, industry, and inefficient energy use, trends that are intensified by rapid urbanization\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.The most vulnerable communities, especially those in informal settlements, face disproportionate health risks due to inadequate housing, limited cooling options, and under-recognized exposure to extreme heat\u003csup\u003e38\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOne promising avenue involves implementing nature-based solutions (NbS), such as urban tree planting, wetland restoration, and green roofing, which can simultaneously lower temperatures and filter pollutants\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. By thoughtfully selecting tree species to avoid high biological VOC emissions, and integrating cleaner technologies (e.g., electric transit systems), cities can address the root causes of both heat and pollution\u003csup\u003e40,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Moreover, stronger regulatory frameworks including tightened vehicle emission standards or industrial air quality controls can ensure that pollution-related hazards are minimized while also advancing climate goals\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCoordinating urban planning and policy across sectors yields the greatest benefits, as evidenced by studies showing that aggressive emission cuts avert thousands of premature deaths related to fine particulate matter and ozone exposure\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Cities such as Nairobi and Lagos have begun integrating climate resilience strategies into broader development plans, adopting clean energy and sustainable transport initiatives to achieve both climate and health co-benefits\u003csup\u003e44\u003c/sup\u003e. Fundamentally, aligning air quality management with climate adaptation and mitigation not only safeguards public health but also fosters more resilient, livable urban environments \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,32,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSeveral limitations of this study are important to highlight. This study was constrained by a relatively short-term dataset covering only four years (2021\u0026ndash;2024), collected from 12 sites in Kigali. Although these measurements provide valuable insights into heatwave\u0026ndash;pollution interactions in a Sub-Saharan African context, the data may not fully capture long-term or interannual climate\u0026ndash;pollution dynamics. Extending both the duration of monitoring and the number of sites would allow for more robust statistical analyses and a better understanding of how climatic trends over time influence air quality patterns in rapidly urbanizing regions.\u003c/p\u003e \u003cp\u003eA second limitation involves the use of low-cost sensors. While these devices were calibrated against a Beta Attenuation Mass Monitor (BAM) station and demonstrated strong correlations (R\u0026thinsp;\u0026gt;\u0026thinsp;0.60, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), uncertainties remain due to inherent sensor variability and the limited availability of reference-grade monitors in the region. Additional reference sites, as well as standardized protocols for sensor calibration, could help refine data quality and improve comparability among different locations and time periods.\u003c/p\u003e \u003cp\u003eIn addition, the meteorological data considered in this study included only temperature and relative humidity. Other factors, such as wind speed, solar radiation, boundary-layer height, and atmospheric pressure, also play essential roles in determining pollutant dispersion and the photochemical processes that lead to elevated ozone. More generally, combining low-cost sensor data with other sources of meteorological, economic, social and land-use data may be a promising approach for future research.\u003c/p\u003e \u003cp\u003eFinally, while this work illustrates how heatwaves can exacerbate air pollution, it does not include data on specific pollution sources or direct health outcomes. Understanding whether PM₂.₅, NO₂, or O₃ originate from traffic, biomass burning, industry, or dust storms, and how these pollutants affect cardiovascular or respiratory health, would strengthen the evidence base for targeted interventions. Studies that integrate robust source apportionment techniques alongside epidemiological data would enable more accurate assessments of risk and the design of tailored mitigation strategies, especially for vulnerable populations.\u003c/p\u003e \u003cp\u003eLooking ahead, longer-term monitoring campaigns with broader geographic coverage, and enhanced meteorological data will help fill current gaps in knowledge. By linking air pollution data to health surveillance records, researchers can quantify the real-world impacts of compound hazards and guide resource allocation for public health interventions. This information, used to inform interdisciplinary efforts involving urban planners, policymakers, and public health practitioners are essential to address the growing challenges posed by rapid urbanization, climate variability, and limited monitoring infrastructure in Sub-Saharan African cities.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Study Area \u0026amp; Data Sources\u003c/h2\u003e \u003cp\u003eRwanda is a landlocked country in Sub-Saharan Africa, with a high population density (503 people per square kilometre) in 2022, which is the second highest in Africa after Mauritius. Rwanda has two main seasons (dry and wet) and experiences a tropical climate with air temperatures between 16\u0026ordm;C and 20\u0026ordm;C. Heatwaves occurring in Rwanda are associated with factors including climate, topography and socio-economic issues that influence the duration and intensity of heatwaves. Due to its thousands of hills and mountains and high altitude, in the past, Rwanda has experienced only rare heatwaves across the country. However, some valleys and urban basins in Kigali, the capital of Rwanda, can trap heat due to weather inversion. Further, the 1994 genocide against the Tutsi created widespread environmental degradation that has made Rwanda vulnerable to extreme climate weather events such as heatwaves. In the last 30 years, Rwanda has undergone rapid urbanization, population growth, and city expansion. This has created urban heat islands in larger cities as a result of high-density building and lack of vegetation. Rwanda also experiences periods of low wind speeds and little rainfall, especially during the wet season, and the Eastern region experiences El Ni\u0026ntilde;o events, causing droughts and floods in the East and Western provinces of Rwanda. Consequently, air quality during heatwaves in Rwanda may vary considerably, especially in Kigali City, the largest city in Rwanda, which suffers from air pollution episodes\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e31\u003c/span\u003e,32\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThis study analyzed PM\u003csub\u003e2.5\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e concentrations obtained from 12 air quality stations in Kigali from May 2021 to December 2024 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e These air quality stations are operated by the HELTH Research Group and the Rwanda Environmental Management Authority (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://aq.rema.gov.rw/\u003c/span\u003e\u003cspan address=\"https://aq.rema.gov.rw/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and hourly averaged pollutant concentrations were analyzed along with meteorological data (daily minimum, mean and maximum temperatures, relative humidity) obtained from the Rwanda Meteorological Agency (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.meteorwanda.gov.rw\u003c/span\u003e\u003cspan address=\"https://www.meteorwanda.gov.rw\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eAir Pollution Measurements and Calibration\u003c/b\u003e \u003c/p\u003e \u003cp\u003eReal-time PM\u003csub\u003e2.5\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e data (measurements over 60 seconds) were collected at 12 sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) in Kigali City using lower-cost, real-time, affordable multi-pollutant (RAMP) monitors from 2021\u0026ndash;2024. The description and calibration of these RAMPs were published in our previous study in Rwanda \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e and detailed by Malings et al\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. The RAMP system (Sensit Technology, USA) units were located 2\u0026ndash;3 m above the ground, using passive electrochemical sensors (Alphasense, UK) to measure five gases and particles (carbon monoxide (CO), nitrogen dioxide (NO₂), ozone (O₃), particulate matter (PM\u003csub\u003e2.5\u003c/sub\u003e) and meteorological parameters (temperature and relative humidity)\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. These sensors collect raw signal data, which is then processed and averaged to produce 60-second air quality measurements. For local data verification in this study, we compared PM\u003csub\u003e2.5\u003c/sub\u003e data from 2021\u0026ndash;2024 from RAMP to data for the same period obtained from a ground-monitored beta attenuation mass monitor (BAM) reference station operated by the US Embassy (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.airnow.gov/\u003c/span\u003e\u003cspan address=\"https://www.airnow.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ) in Kigali from 2022\u0026ndash;2024. This BAM station is 5\u0026ndash;10 km from the RAM air quality station in Kigali. The correlation analysis showed that RAMP and BAM data correlated yearly (r2\u0026thinsp;\u0026gt;\u0026thinsp;0.6, P\u0026thinsp;\u0026lt;\u0026thinsp;0.00). The correlation analysis showed that BAM-PM\u003csub\u003e2.5\u003c/sub\u003e data were significantly positively correlated with RAMP at 60%, suggesting some intra-urban variability associated with local activities, such as traffic, and the distance between stations with data that were not collocated.\u003c/p\u003e \u003cp\u003eDue to data availability, we did not perform local verification of NO\u003csub\u003e2\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e; however, previous studies have conducted quality control and quality assurance using the generalized RAMP (gRAMP) calibration models developed in Pittsburgh\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e46\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHeatwave Definition\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNo universally accepted heatwave definition exists, as climate norms vary widely between regions\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Rwanda is a tropical country in SSA where the average temperature ranges from 16\u0026deg;C to 20\u0026deg;C. Our heatwave definition was based on existing literature\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. We defined a heatwave as three or more consecutive days exceeding the average daily maximum temperature by 5\u0026deg;C, with the daily average determined for the period between 1961 and 1990\u003csup\u003e19\u003c/sup\u003e. From 2021 to 2024, the daily maximum temperature was 25.28\u0026ordm;C for Kigali. We identified six heatwaves with maximum temperatures of 32.3\u0026deg;C \u0026minus;\u0026thinsp;33.5\u0026deg;C. We also defined non-heatwaves periods as days with temperatures below the 90th percentile threshold and not meeting our heatwave definition\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Our heatwave definition of three consecutive or more days with a temperature above 32\u0026deg;C may appear moderate compared to the definitions used in temperate regions. However, in Rwanda's high-altitude areas, including Kigali, the daily maximum temperature can be lower than 16\u0026ndash;20\u0026deg;C. Thus, a sudden increase above 32\u0026deg;C represents almost 12\u0026ndash;16\u0026deg;C above normal and may lead to thermal stress and health impacts. However, no epidemiological study has been conducted in Rwanda or Africa to determine the health outcomes of elevated temperatures. We therefore adopted the approach of using three or more conductive days with temperatures above 32\u0026deg;C, and we believe such thresholds are likely to induce heat-induced physiological stress, especially in vulnerable populations. Our definition aligns with low-resource settings, data gaps and observed weather extremes in the last decade, where the highest temperature ever recorded in Rwanda was 33\u0026deg;C in Kigali, according to the Rwanda Meteorological Agency. Our definition also aligns with several countries in East Africa, such as Uganda, and countries in Asia, such as Vietnam and Thailand, with similar baseline climates and topographies. For example, the neighboring country of Uganda has reported heat-related health warnings in Kampala city at 30\u0026ndash;33\u0026deg;C\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, while Vietnam \u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e considers heatwaves to be temperatures of 32\u0026ndash;35\u0026deg;C for 2 to 3 consecutive days. Therefore, heatwaves should be locally adapted based on population vulnerability, climate, topography, climate norms and health infrastructure. We believe that even a short period (3 days with a temperature above 32\u0026deg;C) may lead to emergency hospital visits, dehydration and stress in Rwanda, although these impacts remain unstudied.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eE.K and A.S contributed equally to the study's conceptualization, methodology, data analysis, manuscript review, and editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe acknowledge the air quality data that the Rwanda Environmental Management Authority provided.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and O\u003csub\u003e3\u003c/sub\u003e data from 2021\u0026ndash;2024, along with meteorological parameters, are available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://aq.rema.gov.rw/\u003c/span\u003e\u003cspan address=\"https://aq.rema.gov.rw/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://meteorwanda.gov.rw/\u003c/span\u003e\u003cspan address=\"https://meteorwanda.gov.rw/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. All the data are reproduced and adapted from the table provided in this study and literature. All data are available on request from the authors.\u003c/p\u003e\u003ch2\u003eCode Availability\u003c/h2\u003e \u003cp\u003eThe code that generated the figures is based on R Language (Versions 4.22 and 4.4.2). It is available upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eIntergovernmental Panel on Climate Change (IPCC). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. https://www.ipcc.ch/report/ar6/wg1/ (2021).\u003c/li\u003e\n\u003cli\u003eWorld Meteorological Organization (WMO). State of the Global Climate 2022. https://wmo.int/publication-series/state-of-global-climate-2022 (2022).\u003c/li\u003e\n\u003cli\u003eKalisa, E., Fadlallah, S., Amani, M., Nahayo, L. \u0026amp; Habiyaremye, G. 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Climatol. \u003cstrong\u003e44\u003c/strong\u003e, 48725-4740 (2024).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Heatwave, Ozone, Particulate matter, Nitrogen dioxide, Sub-Saharan Africa","lastPublishedDoi":"10.21203/rs.3.rs-6346347/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6346347/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDespite mounting evidence that heatwaves aggravate urban air pollution, with substantial impacts on public health, comparatively little research has addressed Sub-Saharan African contexts. In this study, we focused on Kigali, Rwanda, to assess the relationship between extreme heat events and concentrations of fine particulate matter (PM₂.₅), nitrogen dioxide (NO₂), and ozone (O₃) from 2021 to 2024. Using low-cost sensors for dense spatiotemporal coverage, our analysis found that O₃ concentrations increased significantly during 6 heatwave events with peak values up to 40% higher during heatwaves than non-heatwave events in the afternoon. Heatwaves also resulted in spikes in PM\u003csub\u003e2.5\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e, however the diurnal and seasonal analyses showed that PM\u003csub\u003e2.5\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e dynamics were shaped more by local emissions sources than temperature alone. These results highlight the compound risks of heat and air pollution in sub-Saharan African cities, underscoring the importance of early-warning systems and robust urban policies that account for both heat and pollution. In addition, the atmospheric dynamics identified in this research differ from those observed in high-income countries, highlighting a critical need for more research exploring the intersection of heat and air pollution in Sub-Saharan Africa.\u003c/p\u003e","manuscriptTitle":"Heatwaves Amplify Air Pollution Risks in Sub-Saharan Africa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-02 14:30:50","doi":"10.21203/rs.3.rs-6346347/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-27T13:24:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-26T09:48:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-11T16:58:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"27875073345148572148725030572955970768","date":"2025-05-05T10:16:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"24404850402240311844862228411621344530","date":"2025-05-02T05:51:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-28T17:15:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"203385477677803169227296126838790761026","date":"2025-04-16T10:26:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-09T04:25:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-08T06:08:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-08T03:40:04+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-08T03:34:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-03-31T15:48:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9f117168-8aca-4d26-8a53-cca104df309e","owner":[],"postedDate":"April 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46469783,"name":"Earth and environmental sciences/Environmental sciences"},{"id":46469784,"name":"Earth and environmental sciences/Environmental sciences/Environmental impact"},{"id":46469785,"name":"Earth and environmental sciences/Climate sciences/Atmospheric science"},{"id":46469786,"name":"Earth and environmental sciences/Climate sciences/Climate change"}],"tags":[],"updatedAt":"2025-07-28T16:10:00+00:00","versionOfRecord":{"articleIdentity":"rs-6346347","link":"https://doi.org/10.1038/s41598-025-12210-4","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-21 15:57:04","publishedOnDateReadable":"July 21st, 2025"},"versionCreatedAt":"2025-04-02 14:30:50","video":"","vorDoi":"10.1038/s41598-025-12210-4","vorDoiUrl":"https://doi.org/10.1038/s41598-025-12210-4","workflowStages":[]},"version":"v1","identity":"rs-6346347","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6346347","identity":"rs-6346347","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}