Characterizing space-time patterns of nitrogen oxides (NO 2 and NO) pollution in Kigali, Rwanda

Characterizing space-time patterns of nitrogen oxides (NO 2 and NO) pollution in Kigali, Rwanda | 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 Characterizing space-time patterns of nitrogen oxides (NO 2 and NO) pollution in Kigali, Rwanda Pacifique Karekezi, Kate A Kyeremateng, Carissa L Lange, Chantal Umutoni, and 13 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8828727/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Kigali, like many cities in sub-Saharan Africa (SSA), must balance rapid urban growth and the provision of essential services with the need to curb environmental pollution and protect public health in the context of its unique topography. Although the city has implemented policies aimed at reducing emissions from multiple sectors, systematic data on oxides of nitrogen (NO X ; NO 2 and NO), key markers of combustion-related urban air pollution, have been limited. We applied a standardized measurement protocol previously used in Accra, Ghana, to characterize city-scale spatial and temporal patterns of NO X pollution in Kigali. Between November 2022 and December 2023, we deployed Ogawa passive samplers to collect weekly integrated NO 2 (n = 630) and NO (n = 630) samples across 130 sites (10 year-long and 120 rotating week-long locations) representing diverse land-use types and source characteristics. Weekly NO 2 and NO concentrations ranged from approximately 2 to 62 µg/m3 (mean [SD]: 13.9 [11.4]) and approximately 1 to 49 µg/m³, respectively. Although nearly all background sites recorded NO 2 concentrations below the World Health Organization (WHO) annual guideline of 10 µg/m3, exceedances were common in more urbanized settings, occurring in 39% of samples from sparsely residential areas, 89% from commercial, business, and industrial (CBI) areas, and 99% from densely populated residential areas. Mean NO 2 concentrations were significantly higher in urban compared with rural neighborhoods (18.2 vs. 6.3 µg/m³; p < 0.001), at sites located within 200 m of primary roads compared with those farther away (19.9 vs. 11.6 µg/m3; p < 0.001), and at lower compared with higher elevations (15.4 vs. 9.0 µg/m3; p < 0.001). The levels were higher and exceeded the WHO annual guideline in the more densely populated and urbanized districts of Kicukiro and Nyarugenge, compared with the more rural Gasabo district. Similar spatial patterns were observed for NO. Overall, NO 2 and NO concentrations across Kigali were strongly patterned by land use, traffic proximity, population density, and topography, with the highest levels observed in traffic-dominated, densely populated, low-elevation areas. These city-wide measurement data provide critical evidence to inform land-use planning, air quality management, and regulatory strategies in a rapidly urbanizing, landlocked city characterized by complex topography. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Air pollution Nitrogen dioxide Spatial patterns Sub-Saharan Africa Rwanda Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction As sub-Saharan Africa (SSA) cities expand and densify rapidly [ 1 ], the demand for efficient and equitable delivery of essential services, such as transportation and energy, are increasing [ 2 ]. Consequently, many SSA cities are now characterized by heavy traffic congestion and reliance on biomass for household and small-scale commercial activities, both of which are major sources of urban air pollution [ 3 ][ 4 ]. In particular, combustion related air pollutants like oxides of nitrogen (NO x ), composed primarily of nitrogen dioxide (NO 2 ) and nitric oxide (NO) [ 5 ], have become a significant part of the urban air pollutant mixture [ 4 ]. Globally, NO 2 is widely used as a marker of traffic-related air pollution in cities and is regulated at both local and international levels to minimize population exposures [ 6 ]. In the SSA context, sustained household use of biomass fuels in cities is also a known contributor to urban NO x pollution [ 7 ]. Beyond their direct adverse effects on human health, NO x pollution contributes to the formation of secondary particulate matter (PM) and ozone (O 3 ), both of which are independently associated with adverse health outcomes [ 8 ]. As fast-growing SSA cities seek to improve accessibility and promote sustainable mobility and clean energy transitions, they must consider policy interventions that minimize exposure and health impacts from NO x pollution [ 9 ]. However, as with other criteria air pollutants, many SSA cities lack systematic, city-wide NO x monitoring data [ 10 ]. Such data are essential for understanding baseline levels and spatiotemporal patterns, identifying sector- and source-specific contributions, informing regulatory and non-regulatory reduction strategies, and evaluating progress toward achieving air quality targets. Our aim was to examine the space-time patterns of NO 2 and NO pollution in relation to land use and source features across the city of Kigali (Rwanda) to inform future city planning and policy goals. This study was conducted as part of a broader environmental monitoring campaign modeled after the ‘Pathways to Equitable Healthy Cities’ project implemented in Accra, Ghana [ 11 ]. While Kigali’s urban and economic growth patterns resemble those of Accra, its topography, weather, greenery, and policy environment are different. In Kigali, Rwanda’s capital and largest city, and one of the fastest growing cities in East Africa [ 9 ], there are stronger policies and regulations designed to reduce environmental pollution from transportation and commercial and residential sectors [ 12 ]. These include routine vehicle emission inspections and car-free days and zones to promote non-motorized transport and reduce traffic-related emissions [ 12 ] [ 13 ] [ 9 ] [ 14 ]. More broadly, the country has articulated national ambitions to achieve universal electricity access while promoting cleaner household energy options like liquefied petroleum gas (LPG) to reduce reliance on biomass fuels for cooking [ 15 ]. In addition, the country is encouraging the adoption of electric vehicles to curb emissions from diesel- and petrol-powered fleet [ 16 ]. Together, these initiatives have major implications for NO x emissions and patterns across the city, but it lacked comprehensive city-wide data. 2. Methods 2.1 Study location Kigali is among the most densely populated cities in SSA [ 17 ] with a population of ~ 1.7 million, and is still growing at an annual rate of 4% [ 18 ] [ 19 ] [ 20 ]. Located in the central part of Rwanda, Kigali extends over an area of 730 km 2 with unique topography of several hills, valleys and ridges [ 20 ]. The city is divided into three administrative districts and 35 sectors, 22 of which are designated as urban [ 21 ] [ 22 ]. Gasabo, the largest district housing half of Kigali’s population, is predominantly rural; Kicukiro has the highest population density with a mix of urban and rural areas; and Nyarugenge serves as the commercial core [ 20 ] [ 23 ]. The region experiences rainy (March–May, and September–November) and dry (June–August, and December- February) seasons. The temperature and relative humidity ranges from 15 o C – 28 o C (60 o F – 83 o F) and 65% – 85%, respectively, providing a temperate climate conducive to urban living [ 24 ] [ 25 ] . Public transportation in Kigali is mainly provided by buses, motorcycle taxis, and cabs, with a growing presence of electric mobility [ 26 ][ 27 ]. Since 2021, Rwanda has witnessed a notable increase in electric and hybrid vehicles [ 28 ], spurred by government-led incentives aimed at reducing greenhouse gas emissions and promoting environmental sustainability. The city has positioned itself as a regional leader in electric mobility transitions [ 27 ]. However, despite these advances, Kigali still faces air quality challenges due to its location as a landlocked country, and as a result, depends heavily on diesel-powered freight trucks for cross-border trade and goods transport. These heavy-duty vehicles contribute significantly to traffic-related emissions [ 29 ]. Traffic in Kigali can be considered moderate, with congestion primarily occurring during peak hours on major urban corridors [ 30 ]. Vehicle ownership across the country is increasing steadily, with the total number of registered vehicles having grown by approximately 18% since 2021, the majority of which is concentrated in Kigali [ 28 ]. The more urbanized areas have well maintained asphalt roads compared to peri-urban areas. About 80% of households in the city rely on solid biomass (mainly charcoal and wood) for cooking, while the remaining 20% use liquefied petroleum gas (LPG) or electricity [ 21 ]. Household energy choice is heavily influenced by neighborhood socioeconomic status: low-income areas predominantly rely on biomass, whereas higher-income areas primarily use LPG and electricity, although biomass use remains substantial even in these areas [ 31 ]. 2.2 Study design Our design approach followed a previous study conducted in Accra [ 32 ]. Between November 2022 and December 2023, we collected 630 weekly integrated NO x and 630 NO 2 samples at 130 unique locations across Kigali. The sites comprised 10 year-long (‘fixed’) and 120 week-long (‘rotating’) stations, which were carefully selected to represent the various land-use and socioeconomic factors across all three districts (Figure S1 ). While the fixed sites ran continuously throughout the measurement year, the rotating sites were sampled in groups of three per week until all 120 sites were covered. In each measurement week, one of the three rotating sites (33%) had duplicate (side-by-side) measurement and field blank samples for quality control and assurance, resulting in 36 duplicates and 36 blanks. Duplicates assess the precision and reproducibility of measurements, while field blanks detect potential contamination introduced during sampling or analysis, ensuring the accuracy of the reported concentrations. The samplers were mounted on a pole at a height of ~ 4 meters above ground in an unobstructed space. Following the city’s designation, each site was classified as either commercial, business, and industrial (CBI); high-density residential (HDR); low-medium density residential (LDR); or background (BKG). CBI areas are busiest areas with people, along major roads with heavy traffic whereas HDR are densely populated areas with narrow paved or unpaved roads, low socioeconomic status, and extensive biomass use. LDR areas feature formal housing, asphalts roads, higher socioeconomic status, and minimal biomass use. BKG areas have more green spaces, agricultural activities, and limited traffic, but with mixed land use in some parts. 2.3 NO x and NO 2 measurements We used Ogawa passive samplers (Ogawa & Co., Inc., USA) [ 33 ] to capture weekly integrated ambient NO x and NO 2 concentrations on pre-coated collection pads. In the field, the samplers were weather shielded using an opaque plastic container. After sampling, the filters were sealed in vials, refrigerated, and cold-couriered to the University of Massachusetts Amherst for laboratory analysis. Analytical procedures followed Ogawa’s standardized protocol, as described previously [ 32 ]. In summary, final weekly integrated NO x and NO 2 concentrations were quantified using linear calibration curves developed from nitrite standard solutions (Thermo Fisher, USA) [ 32 ] and corrected for local temperature and relative humidity during each measurement week. We reported the final concentrations in µg/m 3 . 2.4 Data management and statistical analysis We estimated NO concentrations as the difference between NO x and NO 2 (i.e. NO = NO x -NO 2 ). Both NO x and NO 2 measurements were blank-corrected, and limits of detection (LOD) were calculated as three times the standard deviation (SD) of the field blanks. The LODs were 0.02 µg/m 3 for NO x and 0.005 µg/m 3 for NO 2 . Duplicate samples were strongly correlated ( R 2 = 0.99 for NO x and 0.98 for NO 2 ) and hence were averaged to provide a single concentration at those sampling locations. To allow for site-to-site comparisons, the individual weekly samples from the 120 rotating sites, which were collected in groups of three per week across the measurement year, were seasonally/temporally adjusted, following the process described in the Accra study [ 32 ]. Briefly, the weekly average NO 2 or NO concentration in a measurement week was adjusted using the ratio of the weekly average data at all fixed (yearlong) sites in that measurement week to the annual mean concentrations across all fixed sites. This process produced annual equivalent concentrations at each rotating site for comparison across the city. The seasonally/temporally adjusted data from the rotating sites were used to characterize the spatial patterns in terms of land use, source features, and administrative districts, whereas the year-long data from the ten fixed sites were used to evaluate the temporal patterns in terms of annual, seasonal, and weekly means. A total of 1,260 NO 2 and NO x samples were collected during the monitoring period. Following data cleaning, 120 (60 NO 2 and 60 NO x ) samples were excluded due to measurement errors. All descriptive statistical tests of significance used an alpha level of 0.05. Data analyses, visualizations, and summary statistics were performed in R-Studio (R version 4.5.0). 3. Results Our final analysis included a total of 570 NO 2 and 570 NO weekly integrated samples, consisting of 3,962 site-days of data collected at the ten fixed (yearlong) and 106 rotating (weeklong) sites. Across space and time, the individual weekly samples ranged from ~ 2 to 62 µg/m 3 for NO 2 and ~ 1 to 49 µg/m 3 for NO. 3.1 Spatial patterns When restricted to data from the rotating sites, the city-wide mean (SD) annual equivalent NO 2 and NO concentrations were 12.6 (7.6) µg/m 3 and 4.13 (4.35) µg/m 3 , respectively. The annual equivalent mean NO 2 concentrations ranged from ~ 5 µg/m 3 at background (BKG) sites to ~ 19 µg/m 3 at HDR sites (Table 1 ), and the difference across land use was statistically significant ( p < 0.01). Levels were highest in CBI and HDR areas, but only HDR differed significantly from LDR neighborhoods, while CBI did not (Table S1 ). The mean NO 2 concentrations at background sites were at least 3-fold lower than at HDR, CBI, and LDR areas. Consistent with NO 2 , NO concentrations were substantially highest in CBI and HDR areas, followed by sites in LDR neighborhoods, and were lowest in BKG areas. However, the NO/NO x ratio did not differ significantly across land-use categories ( p = 0.17) (Table S2), although the highest ratios were observed in CBI and BKG sites, followed by HDR and LDR (Table 1 ). Sites located in urban neighborhoods were approximately three times more polluted than rural areas (18.2 vs. 6.3 µg/m 3 , p < 0.001 for NO 2 ; and 6.2 vs. 1.9 µg/m 3 , p < 0.001 for NO) (Fig. 1 ). Most (~ 92%) of the NO 2 concentrations observed at urban areas, along with ~ 79% of CBI, 96% of HDR, and 61% of LDR areas exceeded the WHO’s annual guideline of 10 µg/m 3 (Fig. 2 , Table S3). Only 12% of samples from rural areas exceeded the WHO guideline, and no sample from BKG sites surpassed this threshold (Fig. 2 ). When samples from fixed sites were Table 1 Summary statistics of NO 2 , NO, NO x and NO/NO x concentrations by land-use category and site-type. Site-type (no. of sites) No. of weekly samples NO 2 NO NO x NO/ NO X Mean (SD) Range NO Range NOx Range NO/NOx Range All sites (126) 570 13.9 (11.4) 1.27–61.95 6.2 (8.79) 0.03–48.45 20.1 (18.8) 1.64–90.45 0.26 (0.14) < 0.01–0.84 Fixed sites (10) 464 14.2 (12.1) 1.27–61.95 6.67 (9.46) 0.03–48.45 20.9 (20.2) 1.64–90.45 0.26 (0.14) < 0.0.01–0.84 CBI (3) 140 26.8 (13.3) 5.58–61.95 16.3 (12.4) 0.25–48.45 43.1 (22.6) 8.01–90.45 0.34 (0.15) 0.02–0.82 HDR (1) 44 20.7 (4.7) 13.87–33.1 6.61 (1.92) 1.75–9.42 27.3 (4.2) 22.21–38.18 0.25 (0.08) 0.07–0.39 LDR (3) 147 9.3 (2.6) 4.27–18.2 2.32 (1.51) 0.03–12.49 11.7 (2.7) 6.14–21.62 0.20 (0.11) < 0.01–0.58 BKG (3) 133 4.3 (2.3) 1.27–13.68 1.35 (1.09) 0.03–7.1 5.6 (2.6) 1.64–15.28 0.24 (0.15) < 0.01–0.84 Rotating sites (106) 106 12.6 (7.6) 1.38–31.95 4.13 (4.35) 0.7–29.95 16.7 (10.5) 2.19–57.58 0.24 (0.11) 0.06–0.53 CBI (14) 14 16.23 (6.63) 4.75–27.63 6.89 (7.89) 0.7–29.95 23.12 (13.19) 5.65–57.58 0.25 (0.13) 0.06–0.52 HDR (22) 22 18.5 (5.25) 5.72–31.95 6.21 (4.04) 1.24–20.42 24.71 (7.16) 6.96–40.25 0.24 (0.09) 0.06–0.51 LDR (41) 41 13.84 (6.92) 1.67–29.08 3.86 (3.22) 0.8–15.64 17.7 (8.56) 2.47–32.7 0.22 (0.11) 0.07–0.51 BKG (29) 29 4.63 (2.04) 1.38–9.31 1.61 (1.01) 0.81–5.71 6.24 (2.34) 2.19–10.82 0.27 (0.11) 0.12–0.53 included, the proportion exceeding the WHO guideline rose to 89%, 99%, and 39% at CBI, HDR, and LDR locations, respectively; only 3.7% of all BKG samples exceeded this guideline. Sites located near primary roads exhibited higher concentrations of both NO₂ and NO, leading to an overall inverse relationship between distance and pollutant. For example, sites within 200 m of primary roads had higher mean NO₂ concentrations than sites located farther away (19.9 vs. 11.6 µg/m 3 ; p < 0.001). NO concentrations were also elevated at sites within 200 m of primary roads (7.5 vs. 3.8 µg/m³; p = 0.08) (Fig. 3 , Figure S2, Table S4). By topography, mean NO₂ concentrations at high-elevation sites (defined as > 1,483 m, the median elevation of Kigali) were substantially lower than at low-elevation sites (9.0 vs. 15.4 µg/m 3 ; p < 0.001). A similar topographical pattern was observed for NO (2.9 vs. 5.4 µg/m 3 ; p = 0.003). Car-free Sundays occur twice per month and last only two hours, making it challenging to assess their impacts using weekly integrated measurements. Nevertheless, we sought to explore its potential influence on pollutant concentrations. At one fixed site (AIMS), located along a traffic diversion route during car-free days, mean NO₂ (30 vs. 27 µg/m 3 ) and NO (18 vs. 16 µg/m 3 ) concentrations were slightly higher during car-free weeks compared with non–car-free weeks. In contrast, at another fixed site (Nyabugogo), the city’s main bus and transport hub, mean NO₂ (39 vs. 42 µg/m 3 ) and NO (30 vs. 29 µg/m 3 ) concentrations decreased slightly during car-free weeks. Overall, we observed limited evidence of consistent changes in NOₓ pollution associated with car-free Sundays. District-level comparisons revealed no statistically significant differences in either the mean NO 2 or mean NO concentrations across the three administrative districts ( p > 0.05). Despite this overall statistical homogeneity, median NO 2 concentrations were higher and exceeded the WHO annual guideline in the densely populated and more urbanized Kicukiro and Nyarugenge districts compared to the rural Gasabo. Outlier analysis showed that several NO concentrations within each district (mostly collected at steep slopes, markets, and areas with high vehicle congestion) deviated substantially from the interquartile range (Fig. 4 ). 3.2 Temporal patterns Across the ten fixed sites that sampled continuously throughout the measurement year, the annual mean (SD) NO 2 and NO concentrations were 14 (12) µg/m 3 and 7 (10) µg/m 3 . These ranged from site-specific annual means of 4.3 µg/m 3 (for NO 2 ) and 1.4 µg/m 3 (for NO) at BKG sites, to 27 µg/m 3 (for NO 2 ) and 16 µg/m 3 (NO) at CBI sites (Table 1 ). All (100%) individual weekly integrated NO 2 samples at HDR sites, and 90% at CBI sites, as well as their site-specific annual means exceeded the WHO’s guideline (Fig. 5 ). In contrast, only a third (33%) of all LDR and 5% of all BKG samples exceeded this threshold, and their respective annual means remained below the guideline (Table S3). Fixed sites NO 2 and NO concentrations showed stronger variations by land use, with all pairwise comparisons statistically significant ( p < 0.001) (Table S1 ) and NO/NO x ratio also highest at CBI sites (Table S2, Figure S5). Even among CBI sites, concentrations at traffic locations were substantially higher than non-traffic sites. For instance, the traffic sites, Nyabugogo recorded the highest average NO 2 (40.2 µg/m 3 ) and NO (29.3 µg/m 3 ) concentrations, followed by AIMS (28.1 µg/m 3 for NO 2 and 16.5 µg/m 3 for NO) and Gahanga market (12.0 µg/m 3 NO 2 and 3.34 µg/m 3 NO) (Figure S3). We observed modest but meaningful seasonal differences in pollutant concentrations. NO 2 levels were higher during the dry season compared to the rainy season (15.4 vs 12.9 µg/m 3 ; p = 0.04). In contrast, NO concentrations were higher in the rainy season than in the dry season (8.22 vs. 5.18 µg/m 3 , p < 0.001) (Fig. 5 , S3, S4, Table S5). 4. Discussion Kigali (and Rwanda as whole) has adopted multi-faceted policy approaches aimed at reducing environmental pollution and promoting environmental sustainability across major emission sectors, including energy, transportation, and residential [ 34 ][ 35 ]. These ongoing interventions have direct implications for combustion related air pollutants like NO x . Yet, there is no systematic and city-scale NO x data on the current levels and patterns as well as sector- and source-specific influences to evaluate progress and guide future policy efforts. Using a dense network of monitors in a year-long city-wide measurement campaign, we found that NO 2 and NO pollution across Kigali City are strongly associated with land use and source features, with traffic dominated, more urbanized, densely populated, and lower elevation areas having the highest concentrations. Nearly all NO 2 data collected at urban, CBI, HDR, and LDR areas exceeded the WHO guideline. We also found that meteorological factors (dry vs wet seasons) have small but significant influence on NOₓ concentrations, elevating NO 2 levels during the dry season and NO during the rainy season. Our Kigali study followed a previous design approach used in Accra, Ghana’s capital [ 11 ][ 32 ]. While both cities exhibit similar patterns of urban and economic growth, there are major differences between them. Kigali is roughly one-third the size of Accra, with fewer people, fewer cars, and relatively milder traffic congestion. Kigali also has more peri-urban spaces, a cooler and greener climate with year-round rainfall, and a hilly, undulating terrain compared to Accra’s flat, low-lying landscape [ 32 ][ 36 ]. Being in East Africa, it does not experience the same periodic Harmattan dust storms like Accra and other parts of West African regions. Additionally, the Rwandan government appears more proactive regarding environmental protection policies and actions. For instance, open burning of solid and residential waste is banned in Kigali [ 37 ] but is commonplace in Accra [ 38 ]. These differences are reflected in the observed pollution levels, as NO x concentrations across all land-use types in Accra are about three to four times higher than those in Kigali, and annual NO and NO 2 levels observed in Kigali were, respectively, approximately 10- and 5-fold lower in Accra. The higher levels observed in Accra likely reflect the larger volume of cars and commercial and industrial activities, as well as trash and electronic waste burning [ 32 ]. Conversely, Kigali’s relatively lower concentrations may be attributable to stricter enforcement of environmental policy and actions. Additionally, although the seasonal effects of NO 2 and NO are consistent in both cities, the magnitude of the impact is much stronger in Accra [ 32 ], which could be explained by the differential role of meteorology in West Africa (with periodic Harmattan) compared to East Africa. Despite these differences, the overall spatial, temporal, and seasonal patterns in Kigali are consistent with those in Accra [ 32 ]. In both cities, NO and NO 2 levels are associated with land use features, road traffic emissions and urbanicity, and residents living in the more urbanized and densely populated neighborhoods are at higher risk of exposure and potential related adverse health effects [ 32 ]. Together, the data from both cities indicate how city planning, zoning and environmental regulation can be used to curb combustion related emissions and population exposure, as both cities expand and densify. Consistent with previous smaller studies in Kigali, NO 2 levels in most parts of the city exceeded the WHO annual guideline [ 36 ]. In particular, densely populated residential areas exhibited concentrations comparable to those in CBI areas for both pollutants, indicating that a large share of Kigali residents are exposed to substantial vehicular emissions. This presents a significant urban planning challenge, as many residents live in HDR areas, which are predominantly unplanned settlements [ 39 ]. Furthermore, we observed highly varied NO x patterns at some relatively affluent and sparsely populated communities, where NO concentrations were comparable to those in both CBI and BKG areas. This variability may reflect emerging LDR areas with ongoing construction sites or areas near congested roads, which are subject to heavy-duty vehicle emissions [ 40 ]. As anticipated, the BKG areas recorded the lowest NO and NO 2 levels, reflecting minimal local influences from traffic and suggesting that emissions from other sources such as agriculture are relatively minor. These low levels also support the effectiveness of local regulations prohibiting the open burning of agricultural and residential waste [ 37 ]. NOₓ concentrations were consistently highest in lower-elevation areas of Kigali, reflecting the role of the city’s hilly topography and low-lying valleys in shaping pollutant dispersion. These areas are more prone to reduced vertical mixing, particularly under stagnant meteorological conditions and temperature inversions, which can trap traffic-related emissions near the surface. In the presence of strong solar radiation and abundant volatile organic compounds (VOCs) from vehicular traffic and fuel use, elevated NOₓ can drive photochemical reactions leading to the formation of ground-level ozone (O 3 ). Exposure to both NO x and O 3 has been linked to adverse respiratory and cardiovascular outcomes, underscoring the public health relevance of pollutant accumulation in Kigali’s low-elevation neighborhoods. In the urban SSA context, smaller studies from Bamako [ 41 ], Abidjan [ 42 ], Cape Town [ 43 ], Kampala [ 44 ], Nairobi [ 45 ], and Addis Ababa [ 46 ] reported NO x levels higher than those observed in Kigali. In those studies, too, elevated NOₓ concentrations were consistently reported near traffic-dominated and in densely populated residential areas, patterns that closely mirror those observed in Kigali and Accra. Within CBI areas, we found that sites located near major roads dominated by heavy-duty trucks and persistent congestion consistently exhibited the highest NO x concentrations. For instance, Nyabugogo, the city’s busiest transport hub and a major interchange for buses, trucks, and freight vehicles, recorded the highest NO x levels, consistent with its position at the convergence of several national highways. These observations aligned with previous studies from Kigali and other African cities, all of which consistently report markedly elevated NO x concentrations near major roads and high-traffic corridors [ 36 ][ 46 ]. Seasonal variation is also evident across the continent, with most studies reporting higher NO 2 concentrations during the dry season like we found in Kigali [ 47 ]. We observed clear seasonal patterns, with NO 2 concentrations elevated during the dry season and NO concentrations peaking in the rainy season. During the rainy season, cloud cover reduces sunlight, slowing the photochemical conversion of NO to NO 2 , while rain removes soluble NO 2 through wet deposition, leading to higher NO and lower NO 2 levels. In the dry season, stronger sunlight accelerates NO oxidation, and the lack of rain allows NO 2 to accumulate, resulting in higher NO 2 and lower NO [ 48 ][ 32 ]. Similar seasonal dynamics have been reported in Kenya [ 47 ] and several West African cities [ 38 ], including in Accra, influenced by factors such as biomass burning, atmospheric dispersion, and photochemical activity during dry periods. Studies from European countries show similar spatial patterns but distinct temporal trends in NO 2 concentrations. The highest levels are consistently reported at roadside locations, along heavily trafficked roads, and in densely populated or industrial areas [ 49 ] but with peaks during winter season [ 50 ][ 49 ][ 51 ][ 52 ]. Overall, while NO x levels have been declining in many European and North American cities in recent years, largely owing to stringent air quality regulations and emission control policies [ 53 ], the levels in SSA cities appear to be increasing with the ongoing economic and population expansion. This calls for strong urban planning and zoning policies in growing SSA cities. 5. Strengths and Limitations This study provides a valuable contribution to the regional evidence base. By sampling nearly all administrative cells across Kigali, we generated a city-scale dataset that enabled a detailed assessment of spatial variability across land-use categories and identification of potential pollution sources and sectors. Our measurement relied on the filter-based Ogawa passive samplers, which are well-established and validated internationally. This further strengthens the reliability and comparability of our data, and offers a clear advantage over many emerging low-cost sensor approaches. Moreover, by employing the same study protocol that was previously implemented in Accra, our data allow for a unique comparison with another major SSA city. Despite these strengths, our study also has some limitations. First, reliance on weekly sampling restricted our ability to evaluate finer temporal dynamics, such as diurnal fluctuations or differences between weekdays and weekends, which are important for understanding short-term pollution episodes and emission patterns. Consequently, we could not accurately examine the role of car-free days policy on NO x pollution in the city. Second, land-use categories were aggregated into only four broad groups, limiting our ability to distinguish contributions from specific emission sources such as industrial activities or agricultural zones. Third, the monitoring period spanned only a single year, constraining the assessment of longer-term trends, inter-annual variability, and the influence of atypical meteorological conditions. Future research that incorporates continuous or higher temporal-resolution monitoring, more detailed land-use classifications, and multi-year measurement campaigns would provide a more comprehensive understanding of air pollution dynamics in Kigali. Additionally, space-time modeling to quantify the influence of land-use, meteorological, and climate variables on NO x levels to determine the contribution of different emission sources, along with assessments of health impacts would further elucidate the drivers of air pollution and their implications for population health. 6. Conclusion Curbing environmental pollution and protecting public in the face of urban and economic growth in Kigali’s unique geography and topography is a priority for both the local and national government. Accordingly, the city is proactively implementing strong policies and regulations to reduce pollution across the transportation and commercial and residential sectors. Our city-wide measurements show that NO 2 pollution is mostly above international health-based guidelines and are strongly influenced by land use, source features, and season. City planning and policy actions targeted at these factors will reduce emissions and protect population exposure and associated health impacts. Declarations Author Contributions Pacifique Karekezi: Conceptualization, data curation, formal analysis, visualization, methodology, writing-original draft, writing-review & edits. Kate A Kyeremateng: Formal analysis, methodology, and writing-review & edits. Carissa L Lange: Formal analysis, methodology, and writing-review & edits. Chantal Umutoni: Data curation, writing-review & edits. Jean Remy Kubwimana: Data curation, writing-review & edits. James Nimo: Data curation, writing-review & edits. Barbara E. Mottey: Data curation, writing-review & edits. Jiayuan Wang: Data curation, writing-review & edits. Silas S. Mirau: Writing-review & edits and Supervision. Claudette Nyinawumuntu: Data curation, writing-review & edits. Samson Niyizurugero: Data curation, writing-review & edits. Pie-Celestin Hakizimana: Site access, writing-review & edits. Isambi S. Mbalawata: Project administration, resources and writing-review & edits. Paterne Gahungu: Conceptualization, data curation, writing-review & edits and Supervision. Majid Ezzati: Conceptualization, writing-review & edits. Allison F. Hughes: Data curation, writing-review & edits. Raphael E. Arku: Conceptualization, formal analysis, methodology, project administration, resources, writing-review & edits and Supervision. Data Availability Data are available upon request from the corresponding author. Funding Sources This study was funded by the Pathways to Equitable Healthy Cities grant (209376/Z/17/Z) from the Wellcome Trust, and GCRF Digital Innovation for Development in Africa network grant [EP/T029145/1] from UKRI. ISM, ME and RA are supported by Digital Innovation for Development in Africa grant (227779/Z/23/Z) from the Wellcome Trust. RA is also supported by the Health Effects Institutes' Rosenblith New Investigator Award (No. CR-83590201). Declaration of competing interests The authors declare no competing financial interests or personal relationship that could have appeared to influence the work reported in this paper. Acknowledgements We thank the University of Ghana team for providing technical support, and the Rwanda Environmental Management Authority (REMA) for approving this project. We also thank the African Institute for Mathematical Sciences Research and Innovation Centre (AIMS-RIC) and the Office for National Statistics (ONS) for funding support. Additionally, we are grateful to the leaders and residents of Kigali City for their collaboration in allowing the installation of monitors on office compounds, public infrastructure and residential properties. 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The dashed horizontal line represents the WHO annual guideline.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8828727/v1/2de69db1b5e317448e3c90c1.png"},{"id":103248153,"identity":"2b15049c-f07f-4600-93d1-3747ae79ba9c","added_by":"auto","created_at":"2026-02-23 15:27:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":129512,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between proximity to primary roads and pollutant concentrations.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8828727/v1/551825d32fcd110839305a48.png"},{"id":103248089,"identity":"2de63095-c1ec-4274-9da0-8e837de5e322","added_by":"auto","created_at":"2026-02-23 15:26:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":76805,"visible":true,"origin":"","legend":"\u003cp\u003eViolin plots showing the distribution of NO\u003csub\u003e2\u003c/sub\u003e (Blue) and NO concentrations across the three administrative districts: Gasabo (n=52), Kicukiro (n=26), and Nyarugenge (n=28). The horizontal dashed line depicts the WHO NO\u003csub\u003e2\u003c/sub\u003e annual guideline of 10 µg/m\u003csup\u003e3\u003c/sup\u003e. \u0026nbsp;\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8828727/v1/71c0af4cb3ec203ffe08e7bd.png"},{"id":103248152,"identity":"01ca6859-e67f-4262-8e14-1f297000bfa7","added_by":"auto","created_at":"2026-02-23 15:27:10","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":148337,"visible":true,"origin":"","legend":"\u003cp\u003eWeekly NO\u003csub\u003e2\u003c/sub\u003e (A) and NO (B) mean concentrations at the ten fixed sites by land use types. The horizontal dashed line in (A) shows the comparison of NO\u003csub\u003e2\u003c/sub\u003e levels with WHO guideline of 10 µg/m\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8828727/v1/a8d61f2c6b588bac6ded9642.png"},{"id":103510955,"identity":"b161b4bd-6c58-42de-868f-af5cb98ea4bf","added_by":"auto","created_at":"2026-02-26 14:08:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1691891,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8828727/v1/bc150c63-be51-4ad0-91e7-352d07954307.pdf"},{"id":103248084,"identity":"b1a9218f-b56f-453c-b145-b0d75ff467c5","added_by":"auto","created_at":"2026-02-23 15:26:58","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1516530,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-8828727/v1/4a416cc5bfffb819162547e7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characterizing space-time patterns of nitrogen oxides (NO 2 and NO) pollution in Kigali, Rwanda","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAs sub-Saharan Africa (SSA) cities expand and densify rapidly [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], the demand for efficient and equitable delivery of essential services, such as transportation and energy, are increasing [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Consequently, many SSA cities are now characterized by heavy traffic congestion and reliance on biomass for household and small-scale commercial activities, both of which are major sources of urban air pollution [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e][\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In particular, combustion related air pollutants like oxides of nitrogen (NO\u003csub\u003ex\u003c/sub\u003e), composed primarily of nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e) and nitric oxide (NO) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], have become a significant part of the urban air pollutant mixture [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGlobally, NO\u003csub\u003e2\u003c/sub\u003e is widely used as a marker of traffic-related air pollution in cities and is regulated at both local and international levels to minimize population exposures [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In the SSA context, sustained household use of biomass fuels in cities is also a known contributor to urban NO\u003csub\u003ex\u003c/sub\u003e pollution [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Beyond their direct adverse effects on human health, NO\u003csub\u003ex\u003c/sub\u003e pollution contributes to the formation of secondary particulate matter (PM) and ozone (O\u003csub\u003e3\u003c/sub\u003e), both of which are independently associated with adverse health outcomes [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. As fast-growing SSA cities seek to improve accessibility and promote sustainable mobility and clean energy transitions, they must consider policy interventions that minimize exposure and health impacts from NO\u003csub\u003ex\u003c/sub\u003e pollution [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, as with other criteria air pollutants, many SSA cities lack systematic, city-wide NO\u003csub\u003ex\u003c/sub\u003e monitoring data [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Such data are essential for understanding baseline levels and spatiotemporal patterns, identifying sector- and source-specific contributions, informing regulatory and non-regulatory reduction strategies, and evaluating progress toward achieving air quality targets.\u003c/p\u003e \u003cp\u003eOur aim was to examine the space-time patterns of NO\u003csub\u003e2\u003c/sub\u003e and NO pollution in relation to land use and source features across the city of Kigali (Rwanda) to inform future city planning and policy goals. This study was conducted as part of a broader environmental monitoring campaign modeled after the \u0026lsquo;Pathways to Equitable Healthy Cities\u0026rsquo; project implemented in Accra, Ghana [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. While Kigali\u0026rsquo;s urban and economic growth patterns resemble those of Accra, its topography, weather, greenery, and policy environment are different. In Kigali, Rwanda\u0026rsquo;s capital and largest city, and one of the fastest growing cities in East Africa [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], there are stronger policies and regulations designed to reduce environmental pollution from transportation and commercial and residential sectors [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These include routine vehicle emission inspections and car-free days and zones to promote non-motorized transport and reduce traffic-related emissions [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. More broadly, the country has articulated national ambitions to achieve universal electricity access while promoting cleaner household energy options like liquefied petroleum gas (LPG) to reduce reliance on biomass fuels for cooking [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In addition, the country is encouraging the adoption of electric vehicles to curb emissions from diesel- and petrol-powered fleet [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Together, these initiatives have major implications for NO\u003csub\u003ex\u003c/sub\u003e emissions and patterns across the city, but it lacked comprehensive city-wide data.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study location\u003c/h2\u003e \u003cp\u003eKigali is among the most densely populated cities in SSA [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] with a population of ~\u0026thinsp;1.7\u0026nbsp;million, and is still growing at an annual rate of 4% [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Located in the central part of Rwanda, Kigali extends over an area of 730 km\u003csup\u003e2\u003c/sup\u003e with unique topography of several hills, valleys and ridges [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The city is divided into three administrative districts and 35 sectors, 22 of which are designated as urban [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Gasabo, the largest district housing half of Kigali\u0026rsquo;s population, is predominantly rural; Kicukiro has the highest population density with a mix of urban and rural areas; and Nyarugenge serves as the commercial core [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The region experiences rainy (March\u0026ndash;May, and September\u0026ndash;November) and dry (June\u0026ndash;August, and December- February) seasons. The temperature and relative humidity ranges from 15\u003csup\u003eo\u003c/sup\u003eC \u0026ndash; 28\u003csup\u003eo\u003c/sup\u003eC (60\u003csup\u003eo\u003c/sup\u003eF \u0026ndash; 83\u003csup\u003eo\u003c/sup\u003eF) and 65% \u0026ndash; 85%, respectively, providing a temperate climate conducive to urban living [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] .\u003c/p\u003e \u003cp\u003ePublic transportation in Kigali is mainly provided by buses, motorcycle taxis, and cabs, with a growing presence of electric mobility [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e][\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Since 2021, Rwanda has witnessed a notable increase in electric and hybrid vehicles [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], spurred by government-led incentives aimed at reducing greenhouse gas emissions and promoting environmental sustainability. The city has positioned itself as a regional leader in electric mobility transitions [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. However, despite these advances, Kigali still faces air quality challenges due to its location as a landlocked country, and as a result, depends heavily on diesel-powered freight trucks for cross-border trade and goods transport. These heavy-duty vehicles contribute significantly to traffic-related emissions [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Traffic in Kigali can be considered moderate, with congestion primarily occurring during peak hours on major urban corridors [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Vehicle ownership across the country is increasing steadily, with the total number of registered vehicles having grown by approximately 18% since 2021, the majority of which is concentrated in Kigali [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The more urbanized areas have well maintained asphalt roads compared to peri-urban areas.\u003c/p\u003e \u003cp\u003eAbout 80% of households in the city rely on solid biomass (mainly charcoal and wood) for cooking, while the remaining 20% use liquefied petroleum gas (LPG) or electricity [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Household energy choice is heavily influenced by neighborhood socioeconomic status: low-income areas predominantly rely on biomass, whereas higher-income areas primarily use LPG and electricity, although biomass use remains substantial even in these areas [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study design\u003c/h2\u003e \u003cp\u003eOur design approach followed a previous study conducted in Accra [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Between November 2022 and December 2023, we collected 630 weekly integrated NO\u003csub\u003ex\u003c/sub\u003e and 630 NO\u003csub\u003e2\u003c/sub\u003e samples at 130 unique locations across Kigali. The sites comprised 10 year-long (\u0026lsquo;fixed\u0026rsquo;) and 120 week-long (\u0026lsquo;rotating\u0026rsquo;) stations, which were carefully selected to represent the various land-use and socioeconomic factors across all three districts (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). While the fixed sites ran continuously throughout the measurement year, the rotating sites were sampled in groups of three per week until all 120 sites were covered. In each measurement week, one of the three rotating sites (33%) had duplicate (side-by-side) measurement and field blank samples for quality control and assurance, resulting in 36 duplicates and 36 blanks. Duplicates assess the precision and reproducibility of measurements, while field blanks detect potential contamination introduced during sampling or analysis, ensuring the accuracy of the reported concentrations.\u003c/p\u003e \u003cp\u003eThe samplers were mounted on a pole at a height of ~\u0026thinsp;4 meters above ground in an unobstructed space. Following the city\u0026rsquo;s designation, each site was classified as either commercial, business, and industrial (CBI); high-density residential (HDR); low-medium density residential (LDR); or background (BKG). CBI areas are busiest areas with people, along major roads with heavy traffic whereas HDR are densely populated areas with narrow paved or unpaved roads, low socioeconomic status, and extensive biomass use. LDR areas feature formal housing, asphalts roads, higher socioeconomic status, and minimal biomass use. BKG areas have more green spaces, agricultural activities, and limited traffic, but with mixed land use in some parts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 NO\u003csub\u003ex\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e measurements\u003c/h2\u003e \u003cp\u003eWe used Ogawa passive samplers (Ogawa \u0026amp; Co., Inc., USA) [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] to capture weekly integrated ambient NO\u003csub\u003ex\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e concentrations on pre-coated collection pads. In the field, the samplers were weather shielded using an opaque plastic container. After sampling, the filters were sealed in vials, refrigerated, and cold-couriered to the University of Massachusetts Amherst for laboratory analysis. Analytical procedures followed Ogawa\u0026rsquo;s standardized protocol, as described previously [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In summary, final weekly integrated NO\u003csub\u003ex\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e concentrations were quantified using linear calibration curves developed from nitrite standard solutions (Thermo Fisher, USA) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and corrected for local temperature and relative humidity during each measurement week. We reported the final concentrations in \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data management and statistical analysis\u003c/h2\u003e \u003cp\u003eWe estimated NO concentrations as the difference between NO\u003csub\u003ex\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e (i.e. NO\u0026thinsp;=\u0026thinsp;NO\u003csub\u003ex\u003c/sub\u003e-NO\u003csub\u003e2\u003c/sub\u003e). Both NO\u003csub\u003ex\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e measurements were blank-corrected, and limits of detection (LOD) were calculated as three times the standard deviation (SD) of the field blanks. The LODs were 0.02 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e for NO\u003csub\u003ex\u003c/sub\u003e and 0.005 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e for NO\u003csub\u003e2\u003c/sub\u003e. Duplicate samples were strongly correlated (\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.99 for NO\u003csub\u003ex\u003c/sub\u003e and 0.98 for NO\u003csub\u003e2\u003c/sub\u003e) and hence were averaged to provide a single concentration at those sampling locations.\u003c/p\u003e \u003cp\u003eTo allow for site-to-site comparisons, the individual weekly samples from the 120 rotating sites, which were collected in groups of three per week across the measurement year, were seasonally/temporally adjusted, following the process described in the Accra study [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Briefly, the weekly average NO\u003csub\u003e2\u003c/sub\u003e or NO concentration in a measurement week was adjusted using the ratio of the weekly average data at all fixed (yearlong) sites in that measurement week to the annual mean concentrations across all fixed sites. This process produced annual equivalent concentrations at each rotating site for comparison across the city. The seasonally/temporally adjusted data from the rotating sites were used to characterize the spatial patterns in terms of land use, source features, and administrative districts, whereas the year-long data from the ten fixed sites were used to evaluate the temporal patterns in terms of annual, seasonal, and weekly means.\u003c/p\u003e \u003cp\u003eA total of 1,260 NO\u003csub\u003e2\u003c/sub\u003e and NO\u003csub\u003ex\u003c/sub\u003e samples were collected during the monitoring period. Following data cleaning, 120 (60 NO\u003csub\u003e2\u003c/sub\u003e and 60 NO\u003csub\u003ex\u003c/sub\u003e) samples were excluded due to measurement errors. All descriptive statistical tests of significance used an alpha level of 0.05. Data analyses, visualizations, and summary statistics were performed in R-Studio (R version 4.5.0).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eOur final analysis included a total of 570 NO\u003csub\u003e2\u003c/sub\u003e and 570 NO weekly integrated samples, consisting of 3,962 site-days of data collected at the ten fixed (yearlong) and 106 rotating (weeklong) sites. Across space and time, the individual weekly samples ranged from ~\u0026thinsp;2 to 62 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e for NO\u003csub\u003e2\u003c/sub\u003e and ~\u0026thinsp;1 to 49 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e for NO.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Spatial patterns\u003c/h2\u003e \u003cp\u003eWhen restricted to data from the rotating sites, the city-wide mean (SD) annual equivalent NO\u003csub\u003e2\u003c/sub\u003e and NO concentrations were 12.6 (7.6) \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e and 4.13 (4.35) \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, respectively. The annual equivalent mean NO\u003csub\u003e2\u003c/sub\u003e concentrations ranged from ~\u0026thinsp;5 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e at background (BKG) sites to ~\u0026thinsp;19 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e at HDR sites (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and the difference across land use was statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Levels were highest in CBI and HDR areas, but only HDR differed significantly from LDR neighborhoods, while CBI did not (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The mean NO\u003csub\u003e2\u003c/sub\u003e concentrations at background sites were at least 3-fold lower than at HDR, CBI, and LDR areas. Consistent with NO\u003csub\u003e2\u003c/sub\u003e, NO concentrations were substantially highest in CBI and HDR areas, followed by sites in LDR neighborhoods, and were lowest in BKG areas. However, the NO/NO\u003csub\u003ex\u003c/sub\u003e ratio did not differ significantly across land-use categories (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.17) (Table S2), although the highest ratios were observed in CBI and BKG sites, followed by HDR and LDR (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSites located in urban neighborhoods were approximately three times more polluted than rural areas (18.2 vs. 6.3 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for NO\u003csub\u003e2\u003c/sub\u003e; and 6.2 vs. 1.9 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for NO) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Most (~\u0026thinsp;92%) of the NO\u003csub\u003e2\u003c/sub\u003e concentrations observed at urban areas, along with ~\u0026thinsp;79% of CBI, 96% of HDR, and 61% of LDR areas exceeded the WHO\u0026rsquo;s annual guideline of 10 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table S3). Only 12% of samples from rural areas exceeded the WHO guideline, and no sample from BKG sites surpassed this threshold (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). When samples from fixed sites were\u003c/p\u003e \u003cp\u003e \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\u003eSummary statistics of NO\u003csub\u003e2\u003c/sub\u003e, NO, NO\u003csub\u003ex\u003c/sub\u003e and NO/NO\u003csub\u003ex\u003c/sub\u003e concentrations by land-use category and site-type.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSite-type (no. of sites)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo. of weekly samples\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eNO\u003csub\u003ex\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c10\" namest=\"c9\"\u003e \u003cp\u003eNO/ NO\u003csub\u003eX\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNOx\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNO/NOx\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAll sites (126)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e570\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.9 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.27\u0026ndash;61.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.2 (8.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u0026ndash;48.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20.1 (18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.64\u0026ndash;90.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.26 (0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026ndash;0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFixed sites (10)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14.2 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.27\u0026ndash;61.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.67 (9.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u0026ndash;48.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20.9 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.64\u0026ndash;90.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.26 (0.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0.01\u0026ndash;0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCBI (3)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.8 (13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.58\u0026ndash;61.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.3 (12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.25\u0026ndash;48.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e43.1 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.01\u0026ndash;90.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.34 (0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.02\u0026ndash;0.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHDR (1)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.7 (4.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.87\u0026ndash;33.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.61 (1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.75\u0026ndash;9.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e27.3 (4.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e22.21\u0026ndash;38.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.25 (0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.07\u0026ndash;0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLDR (3)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.3 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.27\u0026ndash;18.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.32 (1.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u0026ndash;12.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11.7 (2.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.14\u0026ndash;21.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.20 (0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026ndash;0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBKG (3)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.3 (2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.27\u0026ndash;13.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.35 (1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u0026ndash;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.6 (2.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.64\u0026ndash;15.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.24 (0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u0026ndash;0.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRotating sites (106)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.6 (7.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.38\u0026ndash;31.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.13 (4.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7\u0026ndash;29.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.7 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.19\u0026ndash;57.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.24 (0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.06\u0026ndash;0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCBI (14)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.23 (6.63)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.75\u0026ndash;27.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.89 (7.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7\u0026ndash;29.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23.12 (13.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5.65\u0026ndash;57.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.25 (0.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.06\u0026ndash;0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHDR (22)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18.5 (5.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.72\u0026ndash;31.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.21 (4.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.24\u0026ndash;20.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24.71 (7.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.96\u0026ndash;40.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.24 (0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.06\u0026ndash;0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLDR (41)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.84 (6.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.67\u0026ndash;29.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.86 (3.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.8\u0026ndash;15.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.7 (8.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.47\u0026ndash;32.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.22 (0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.07\u0026ndash;0.51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBKG (29)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.63 (2.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.38\u0026ndash;9.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.61 (1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.81\u0026ndash;5.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.24 (2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.19\u0026ndash;10.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.27 (0.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.12\u0026ndash;0.53\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\u003eincluded, the proportion exceeding the WHO guideline rose to 89%, 99%, and 39% at CBI, HDR, and LDR locations, respectively; only 3.7% of all BKG samples exceeded this guideline.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSites located near primary roads exhibited higher concentrations of both NO₂ and NO, leading to an overall inverse relationship between distance and pollutant. For example, sites within 200 m of primary roads had higher mean NO₂ concentrations than sites located farther away (19.9 vs. 11.6 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). NO concentrations were also elevated at sites within 200 m of primary roads (7.5 vs. 3.8 \u0026micro;g/m\u0026sup3;; p\u0026thinsp;=\u0026thinsp;0.08) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Figure S2, Table S4). By topography, mean NO₂ concentrations at high-elevation sites (defined as \u0026gt;\u0026thinsp;1,483 m, the median elevation of Kigali) were substantially lower than at low-elevation sites (9.0 vs. 15.4 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A similar topographical pattern was observed for NO (2.9 vs. 5.4 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCar-free Sundays occur twice per month and last only two hours, making it challenging to assess their impacts using weekly integrated measurements. Nevertheless, we sought to explore its potential influence on pollutant concentrations. At one fixed site (AIMS), located along a traffic diversion route during car-free days, mean NO₂ (30 vs. 27 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) and NO (18 vs. 16 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) concentrations were slightly higher during car-free weeks compared with non\u0026ndash;car-free weeks. In contrast, at another fixed site (Nyabugogo), the city\u0026rsquo;s main bus and transport hub, mean NO₂ (39 vs. 42 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) and NO (30 vs. 29 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) concentrations decreased slightly during car-free weeks. Overall, we observed limited evidence of consistent changes in NOₓ pollution associated with car-free Sundays.\u003c/p\u003e \u003cp\u003eDistrict-level comparisons revealed no statistically significant differences in either the mean NO\u003csub\u003e2\u003c/sub\u003e or mean NO concentrations across the three administrative districts (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Despite this overall statistical homogeneity, median NO\u003csub\u003e2\u003c/sub\u003e concentrations were higher and exceeded the WHO annual guideline in the densely populated and more urbanized Kicukiro and Nyarugenge districts compared to the rural Gasabo. Outlier analysis showed that several NO concentrations within each district (mostly collected at steep slopes, markets, and areas with high vehicle congestion) deviated substantially from the interquartile range (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Temporal patterns\u003c/h2\u003e \u003cp\u003eAcross the ten fixed sites that sampled continuously throughout the measurement year, the annual mean (SD) NO\u003csub\u003e2\u003c/sub\u003e and NO concentrations were 14 (12) \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e and 7 (10) \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e. These ranged from site-specific annual means of 4.3 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (for NO\u003csub\u003e2\u003c/sub\u003e) and 1.4 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (for NO) at BKG sites, to 27 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (for NO\u003csub\u003e2\u003c/sub\u003e) and 16 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (NO) at CBI sites (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). All (100%) individual weekly integrated NO\u003csub\u003e2\u003c/sub\u003e samples at HDR sites, and 90% at CBI sites, as well as their site-specific annual means exceeded the WHO\u0026rsquo;s guideline (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In contrast, only a third (33%) of all LDR and 5% of all BKG samples exceeded this threshold, and their respective annual means remained below the guideline (Table S3).\u003c/p\u003e \u003cp\u003eFixed sites NO\u003csub\u003e2\u003c/sub\u003e and NO concentrations showed stronger variations by land use, with all pairwise comparisons statistically significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) and NO/NO\u003csub\u003ex\u003c/sub\u003e ratio also highest at CBI sites (Table S2, Figure S5). Even among CBI sites, concentrations at traffic locations were substantially higher than non-traffic sites. For instance, the traffic sites, Nyabugogo recorded the highest average NO\u003csub\u003e2\u003c/sub\u003e (40.2 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) and NO (29.3 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) concentrations, followed by AIMS (28.1 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e for NO\u003csub\u003e2\u003c/sub\u003e and 16.5 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e for NO) and Gahanga market (12.0 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e NO\u003csub\u003e2\u003c/sub\u003e and 3.34 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e NO) (Figure S3).\u003c/p\u003e \u003cp\u003eWe observed modest but meaningful seasonal differences in pollutant concentrations. NO\u003csub\u003e2\u003c/sub\u003e levels were higher during the dry season compared to the rainy season (15.4 vs 12.9 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04). In contrast, NO concentrations were higher in the rainy season than in the dry season (8.22 vs. 5.18 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, S3, S4, Table S5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eKigali (and Rwanda as whole) has adopted multi-faceted policy approaches aimed at reducing environmental pollution and promoting environmental sustainability across major emission sectors, including energy, transportation, and residential [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e][\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. These ongoing interventions have direct implications for combustion related air pollutants like NO\u003csub\u003ex\u003c/sub\u003e. Yet, there is no systematic and city-scale NO\u003csub\u003ex\u003c/sub\u003e data on the current levels and patterns as well as sector- and source-specific influences to evaluate progress and guide future policy efforts. Using a dense network of monitors in a year-long city-wide measurement campaign, we found that NO\u003csub\u003e2\u003c/sub\u003e and NO pollution across Kigali City are strongly associated with land use and source features, with traffic dominated, more urbanized, densely populated, and lower elevation areas having the highest concentrations. Nearly all NO\u003csub\u003e2\u003c/sub\u003e data collected at urban, CBI, HDR, and LDR areas exceeded the WHO guideline. We also found that meteorological factors (dry vs wet seasons) have small but significant influence on NOₓ concentrations, elevating NO\u003csub\u003e2\u003c/sub\u003e levels during the dry season and NO during the rainy season.\u003c/p\u003e \u003cp\u003eOur Kigali study followed a previous design approach used in Accra, Ghana\u0026rsquo;s capital [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e][\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. While both cities exhibit similar patterns of urban and economic growth, there are major differences between them. Kigali is roughly one-third the size of Accra, with fewer people, fewer cars, and relatively milder traffic congestion. Kigali also has more peri-urban spaces, a cooler and greener climate with year-round rainfall, and a hilly, undulating terrain compared to Accra\u0026rsquo;s flat, low-lying landscape [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e][\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Being in East Africa, it does not experience the same periodic Harmattan dust storms like Accra and other parts of West African regions. Additionally, the Rwandan government appears more proactive regarding environmental protection policies and actions. For instance, open burning of solid and residential waste is banned in Kigali [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] but is commonplace in Accra [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. These differences are reflected in the observed pollution levels, as NO\u003csub\u003ex\u003c/sub\u003e concentrations across all land-use types in Accra are about three to four times higher than those in Kigali, and annual NO and NO\u003csub\u003e2\u003c/sub\u003e levels observed in Kigali were, respectively, approximately 10- and 5-fold lower in Accra. The higher levels observed in Accra likely reflect the larger volume of cars and commercial and industrial activities, as well as trash and electronic waste burning [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Conversely, Kigali\u0026rsquo;s relatively lower concentrations may be attributable to stricter enforcement of environmental policy and actions. Additionally, although the seasonal effects of NO\u003csub\u003e2\u003c/sub\u003e and NO are consistent in both cities, the magnitude of the impact is much stronger in Accra [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], which could be explained by the differential role of meteorology in West Africa (with periodic Harmattan) compared to East Africa. Despite these differences, the overall spatial, temporal, and seasonal patterns in Kigali are consistent with those in Accra [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In both cities, NO and NO\u003csub\u003e2\u003c/sub\u003e levels are associated with land use features, road traffic emissions and urbanicity, and residents living in the more urbanized and densely populated neighborhoods are at higher risk of exposure and potential related adverse health effects [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Together, the data from both cities indicate how city planning, zoning and environmental regulation can be used to curb combustion related emissions and population exposure, as both cities expand and densify.\u003c/p\u003e \u003cp\u003eConsistent with previous smaller studies in Kigali, NO\u003csub\u003e2\u003c/sub\u003e levels in most parts of the city exceeded the WHO annual guideline [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In particular, densely populated residential areas exhibited concentrations comparable to those in CBI areas for both pollutants, indicating that a large share of Kigali residents are exposed to substantial vehicular emissions. This presents a significant urban planning challenge, as many residents live in HDR areas, which are predominantly unplanned settlements [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Furthermore, we observed highly varied NO\u003csub\u003ex\u003c/sub\u003e patterns at some relatively affluent and sparsely populated communities, where NO concentrations were comparable to those in both CBI and BKG areas. This variability may reflect emerging LDR areas with ongoing construction sites or areas near congested roads, which are subject to heavy-duty vehicle emissions [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. As anticipated, the BKG areas recorded the lowest NO and NO\u003csub\u003e2\u003c/sub\u003e levels, reflecting minimal local influences from traffic and suggesting that emissions from other sources such as agriculture are relatively minor. These low levels also support the effectiveness of local regulations prohibiting the open burning of agricultural and residential waste [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNOₓ concentrations were consistently highest in lower-elevation areas of Kigali, reflecting the role of the city\u0026rsquo;s hilly topography and low-lying valleys in shaping pollutant dispersion. These areas are more prone to reduced vertical mixing, particularly under stagnant meteorological conditions and temperature inversions, which can trap traffic-related emissions near the surface. In the presence of strong solar radiation and abundant volatile organic compounds (VOCs) from vehicular traffic and fuel use, elevated NOₓ can drive photochemical reactions leading to the formation of ground-level ozone (O\u003csub\u003e3\u003c/sub\u003e). Exposure to both NO\u003csub\u003ex\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e has been linked to adverse respiratory and cardiovascular outcomes, underscoring the public health relevance of pollutant accumulation in Kigali\u0026rsquo;s low-elevation neighborhoods.\u003c/p\u003e \u003cp\u003eIn the urban SSA context, smaller studies from Bamako [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], Abidjan [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], Cape Town [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], Kampala [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], Nairobi [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], and Addis Ababa [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] reported NO\u003csub\u003ex\u003c/sub\u003e levels higher than those observed in Kigali. In those studies, too, elevated NOₓ concentrations were consistently reported near traffic-dominated and in densely populated residential areas, patterns that closely mirror those observed in Kigali and Accra. Within CBI areas, we found that sites located near major roads dominated by heavy-duty trucks and persistent congestion consistently exhibited the highest NO\u003csub\u003ex\u003c/sub\u003e concentrations. For instance, Nyabugogo, the city\u0026rsquo;s busiest transport hub and a major interchange for buses, trucks, and freight vehicles, recorded the highest NO\u003csub\u003ex\u003c/sub\u003e levels, consistent with its position at the convergence of several national highways. These observations aligned with previous studies from Kigali and other African cities, all of which consistently report markedly elevated NO\u003csub\u003ex\u003c/sub\u003e concentrations near major roads and high-traffic corridors [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e][\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeasonal variation is also evident across the continent, with most studies reporting higher NO\u003csub\u003e2\u003c/sub\u003e concentrations during the dry season like we found in Kigali [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. We observed clear seasonal patterns, with NO\u003csub\u003e2\u003c/sub\u003e concentrations elevated during the dry season and NO concentrations peaking in the rainy season. During the rainy season, cloud cover reduces sunlight, slowing the photochemical conversion of NO to NO\u003csub\u003e2\u003c/sub\u003e, while rain removes soluble NO\u003csub\u003e2\u003c/sub\u003e through wet deposition, leading to higher NO and lower NO\u003csub\u003e2\u003c/sub\u003e levels. In the dry season, stronger sunlight accelerates NO oxidation, and the lack of rain allows NO\u003csub\u003e2\u003c/sub\u003e to accumulate, resulting in higher NO\u003csub\u003e2\u003c/sub\u003e and lower NO [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e][\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Similar seasonal dynamics have been reported in Kenya [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] and several West African cities [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], including in Accra, influenced by factors such as biomass burning, atmospheric dispersion, and photochemical activity during dry periods.\u003c/p\u003e \u003cp\u003eStudies from European countries show similar spatial patterns but distinct temporal trends in NO\u003csub\u003e2\u003c/sub\u003e concentrations. The highest levels are consistently reported at roadside locations, along heavily trafficked roads, and in densely populated or industrial areas [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] but with peaks during winter season [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e][\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e][\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e][\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Overall, while NO\u003csub\u003ex\u003c/sub\u003e levels have been declining in many European and North American cities in recent years, largely owing to stringent air quality regulations and emission control policies [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], the levels in SSA cities appear to be increasing with the ongoing economic and population expansion. This calls for strong urban planning and zoning policies in growing SSA cities.\u003c/p\u003e"},{"header":"5. Strengths and Limitations","content":"\u003cp\u003eThis study provides a valuable contribution to the regional evidence base. By sampling nearly all administrative cells across Kigali, we generated a city-scale dataset that enabled a detailed assessment of spatial variability across land-use categories and identification of potential pollution sources and sectors. Our measurement relied on the filter-based Ogawa passive samplers, which are well-established and validated internationally. This further strengthens the reliability and comparability of our data, and offers a clear advantage over many emerging low-cost sensor approaches. Moreover, by employing the same study protocol that was previously implemented in Accra, our data allow for a unique comparison with another major SSA city.\u003c/p\u003e \u003cp\u003eDespite these strengths, our study also has some limitations. First, reliance on weekly sampling restricted our ability to evaluate finer temporal dynamics, such as diurnal fluctuations or differences between weekdays and weekends, which are important for understanding short-term pollution episodes and emission patterns. Consequently, we could not accurately examine the role of car-free days policy on NO\u003csub\u003ex\u003c/sub\u003e pollution in the city. Second, land-use categories were aggregated into only four broad groups, limiting our ability to distinguish contributions from specific emission sources such as industrial activities or agricultural zones. Third, the monitoring period spanned only a single year, constraining the assessment of longer-term trends, inter-annual variability, and the influence of atypical meteorological conditions. Future research that incorporates continuous or higher temporal-resolution monitoring, more detailed land-use classifications, and multi-year measurement campaigns would provide a more comprehensive understanding of air pollution dynamics in Kigali. Additionally, space-time modeling to quantify the influence of land-use, meteorological, and climate variables on NO\u003csub\u003ex\u003c/sub\u003e levels to determine the contribution of different emission sources, along with assessments of health impacts would further elucidate the drivers of air pollution and their implications for population health.\u003c/p\u003e"},{"header":"6. Conclusion","content":"\u003cp\u003eCurbing environmental pollution and protecting public in the face of urban and economic growth in Kigali\u0026rsquo;s unique geography and topography is a priority for both the local and national government. Accordingly, the city is proactively implementing strong policies and regulations to reduce pollution across the transportation and commercial and residential sectors. Our city-wide measurements show that NO\u003csub\u003e2\u003c/sub\u003e pollution is mostly above international health-based guidelines and are strongly influenced by land use, source features, and season. City planning and policy actions targeted at these factors will reduce emissions and protect population exposure and associated health impacts.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePacifique Karekezi: Conceptualization, data curation, formal analysis, visualization, methodology, writing-original draft, writing-review \u0026amp; edits. Kate A Kyeremateng: Formal analysis, methodology, and writing-review \u0026amp; edits. Carissa L Lange: Formal analysis, methodology, and writing-review \u0026amp; edits. Chantal Umutoni: Data curation, writing-review \u0026amp; edits. Jean Remy Kubwimana: Data curation, writing-review \u0026amp; edits. James Nimo: Data curation, writing-review \u0026amp; edits. Barbara E. Mottey: Data curation, writing-review \u0026amp; edits. Jiayuan Wang: Data curation, writing-review \u0026amp; edits. Silas S. Mirau: Writing-review \u0026amp; edits and Supervision. Claudette Nyinawumuntu: Data curation, writing-review \u0026amp; edits. Samson Niyizurugero: Data curation, writing-review \u0026amp; edits. Pie-Celestin Hakizimana: Site access, writing-review \u0026amp; edits. Isambi S. Mbalawata: Project administration, resources and writing-review \u0026amp; edits. Paterne Gahungu: Conceptualization, data curation, writing-review \u0026amp; edits and Supervision. Majid Ezzati: Conceptualization, writing-review \u0026amp; edits. Allison F. Hughes: Data curation, writing-review \u0026amp; edits. Raphael E. Arku: Conceptualization, formal analysis, methodology, project administration, resources, writing-review \u0026amp; edits and Supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are available upon request from the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the \u003cem\u003ePathways to Equitable Healthy Cities\u003c/em\u003e grant (209376/Z/17/Z) from the Wellcome Trust, and GCRF \u003cem\u003eDigital Innovation for Development in Africa\u003c/em\u003e network grant [EP/T029145/1] from UKRI. ISM, ME and RA are supported by \u003cem\u003eDigital Innovation for Development in Africa\u003c/em\u003e grant (227779/Z/23/Z) from the Wellcome Trust. RA is also supported by the Health Effects Institutes' Rosenblith New Investigator Award (No. CR-83590201).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing financial interests or personal relationship that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank the University of Ghana team for providing technical support, and the Rwanda Environmental Management Authority (REMA) for approving this project. We also thank the African Institute for Mathematical Sciences Research and Innovation Centre (AIMS-RIC) and the Office for National Statistics (ONS) for funding support. Additionally, we are grateful to the leaders and residents of Kigali City for their collaboration in allowing the installation of monitors on office compounds, public infrastructure and residential properties.\u003c/p\u003e\n\u003cp\u003eFor the purpose of open Access, the author has applied CC BY public copyright license to any Author Accepted Manuscript version arising from this submission.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eP. Combes, \u0026ldquo;An anatomy of urbanization in sub-Saharan Africa,\u0026rdquo; vol. 115, no. April 2024, 2025.\u003c/li\u003e\n\u003cli\u003eB. O. K. Lokonon, \u0026ldquo;Urbanization and transport energy consumption in African countries,\u0026rdquo; pp. 1\u0026ndash;9, 2023.\u003c/li\u003e\n\u003cli\u003eJ. Scott, \u0026ldquo;The risks of rapid urbanization in developing countries,\u0026rdquo; 2015. .\u003c/li\u003e\n\u003cli\u003eX. Yan, C. Zuo, Z. Li, H. W. Chen, Y. Jiang, B. He, H. Liu, J. Chen, and W. 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Tzoulas, \u0026ldquo;Spatiotemporal variability of nitrogen dioxide ( NO 2 ) pollution in Manchester ( UK ) city centre ( 2017 \u0026ndash; 2018 ) using a fine spatial scale single-NO x diffusion tube network,\u0026rdquo; vol. 0123456789, pp. 3907\u0026ndash;3927, 2022.\u003c/li\u003e\n\u003cli\u003eT. Drosoglou, M. Koukouli, and I. Raptis, \u0026ldquo;Nitrogen dioxide spatiotemporal variations in the complex urban environment of Athens , Greece,\u0026rdquo; vol. 314, no. June, 2023.\u003c/li\u003e\n\u003cli\u003eR. Salas, M. J. Perez-villadoniga, J. Prieto-rodriguez, and A. Russo, \u0026ldquo;Were traffic restrictions in Madrid effective at reducing NO2 levels ?,\u0026rdquo; no. 2.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-clean-air","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Clean Air](https://www.nature.com/npjcleanair/)","snPcode":"44407","submissionUrl":"https://submission.springernature.com/new-submission/44407/3","title":"npj Clean Air","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Air pollution, Nitrogen dioxide, Spatial patterns, Sub-Saharan Africa, Rwanda","lastPublishedDoi":"10.21203/rs.3.rs-8828727/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8828727/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eKigali, like many cities in sub-Saharan Africa (SSA), must balance rapid urban growth and the provision of essential services with the need to curb environmental pollution and protect public health in the context of its unique topography. Although the city has implemented policies aimed at reducing emissions from multiple sectors, systematic data on oxides of nitrogen (NO\u003csub\u003eX\u003c/sub\u003e; NO\u003csub\u003e2\u003c/sub\u003e and NO), key markers of combustion-related urban air pollution, have been limited. We applied a standardized measurement protocol previously used in Accra, Ghana, to characterize city-scale spatial and temporal patterns of NO\u003csub\u003eX\u003c/sub\u003e pollution in Kigali.\u003c/p\u003e \u003cp\u003eBetween November 2022 and December 2023, we deployed Ogawa passive samplers to collect weekly integrated NO\u003csub\u003e2\u003c/sub\u003e (n\u0026thinsp;=\u0026thinsp;630) and NO (n\u0026thinsp;=\u0026thinsp;630) samples across 130 sites (10 year-long and 120 rotating week-long locations) representing diverse land-use types and source characteristics. Weekly NO\u003csub\u003e2\u003c/sub\u003e and NO concentrations ranged from approximately 2 to 62 \u0026micro;g/m3 (mean [SD]: 13.9 [11.4]) and approximately 1 to 49 \u0026micro;g/m\u0026sup3;, respectively. Although nearly all background sites recorded NO\u003csub\u003e2\u003c/sub\u003e concentrations below the World Health Organization (WHO) annual guideline of 10 \u0026micro;g/m3, exceedances were common in more urbanized settings, occurring in 39% of samples from sparsely residential areas, 89% from commercial, business, and industrial (CBI) areas, and 99% from densely populated residential areas. Mean NO\u003csub\u003e2\u003c/sub\u003e concentrations were significantly higher in urban compared with rural neighborhoods (18.2 vs. 6.3 \u0026micro;g/m\u0026sup3;; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), at sites located within 200 m of primary roads compared with those farther away (19.9 vs. 11.6 \u0026micro;g/m3; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and at lower compared with higher elevations (15.4 vs. 9.0 \u0026micro;g/m3; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The levels were higher and exceeded the WHO annual guideline in the more densely populated and urbanized districts of Kicukiro and Nyarugenge, compared with the more rural Gasabo district. Similar spatial patterns were observed for NO.\u003c/p\u003e \u003cp\u003eOverall, NO\u003csub\u003e2\u003c/sub\u003e and NO concentrations across Kigali were strongly patterned by land use, traffic proximity, population density, and topography, with the highest levels observed in traffic-dominated, densely populated, low-elevation areas. These city-wide measurement data provide critical evidence to inform land-use planning, air quality management, and regulatory strategies in a rapidly urbanizing, landlocked city characterized by complex topography.\u003c/p\u003e","manuscriptTitle":"Characterizing space-time patterns of nitrogen oxides (NO 2 and NO) pollution in Kigali, Rwanda","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-23 15:25:53","doi":"10.21203/rs.3.rs-8828727/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-10T16:18:45+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-05T17:53:48+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-01T03:02:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"116945660686501523701795138001566252148","date":"2026-02-21T02:47:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"169438711480494884451067956860369911184","date":"2026-02-19T14:20:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-18T20:41:32+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-16T06:33:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-16T03:36:09+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Clean Air","date":"2026-02-09T09:17:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-clean-air","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Clean Air](https://www.nature.com/npjcleanair/)","snPcode":"44407","submissionUrl":"https://submission.springernature.com/new-submission/44407/3","title":"npj Clean Air","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ea685fdd-3e66-4cb0-a191-6469b8b76ecf","owner":[],"postedDate":"February 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":63291160,"name":"Earth and environmental sciences/Environmental sciences"},{"id":63291161,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2026-05-12T01:08:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-23 15:25:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8828727","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8828727","identity":"rs-8828727","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}