Evaluation of air quality index and particulate matter distribution in Cairo urban environments

While Cairo’s ancient monuments attract millions of visitors each year, the city’s air tells a less celebrated story-one of persistent haze, respiratory illness, and pollution levels that routinely exceed international health benchmarks. This research evaluated the spatial and seasonal distribution of the Air Quality Index (AQI) and particulate matter (PM2.5, PM10) across 32 monitoring stations in Greater Cairo between March 2019 and February 2023. AQI values were computed following the US EPA breakpoint method. Annual mean PM2.5 concentrations ranged from 47.3 μg/m³ at suburban sites to 128.6 μg/m³ near the Helwan industrial corridor-both well above the WHO guideline of 15 μg/m³. PM10 followed a parallel gradient, peaking at 294 μg/m³ during spring khamsin dust episodes. AQI exceeded the “unhealthy” threshold (> 150) on 41.3% of monitored days across all stations. A negative correlation (r = −0.74) between AQI and distance from industrial zones confirmed proximity to point sources as a leading spatial driver. Seasonal decomposition showed that autumn open-rice-straw burning and spring desert dust storms each contributed distinct pollution peaks. Source apportionment using a receptor model attributed 34.8% of ambient PM2.5 to vehicular emissions, 22.1% to industry, and 17.6% to open burning. These findings support the need for emission controls targeting traffic and agricultural burning to bring Cairo’s air quality closer to health-based standards.

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PM2.5 Source DistributionClose ×

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Seasonal Pollution DriversClose ×

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PM2.5 (μg/m³) and PM10 (μg/m³) and more: Traffic vs Industrial vs Residential vs SuburbanClose ×

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PM2.5 and PM10 and more: Spring vs Summer vs Autumn vs WinterClose ×

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Introduction

Cairo is simultaneously one of the world's oldest continuously inhabited cities and one of its most polluted. The World Health Organization has repeatedly ranked it among the top ten megacities for ambient particulate matter concentrations [1] . With a metropolitan population exceeding 21 million, a vehicle fleet growing at 7% per year, and a semi-arid climate that limits pollutant dispersion, Cairo faces air quality challenges that few cities of comparable size have managed to resolve [2,3] . Particulate matter-especially particles smaller than 2.5 μm (PM2.5)-is the pollutant most strongly linked to cardiovascular and respiratory morbidity. The WHO's 2021 guidelines set an annual mean PM2.5 target of 15 μg/m³, but monitoring data from Cairo consistently show values five to eight times higher [4] . The Air Quality Index converts raw pollutant concentrations into a single dimensionless number that communicates health risk to the public on a colour-coded scale [5] . Despite its simplicity, AQI-based assessments for Cairo remain uncommon in the peer-reviewed literature; most published work has focused on either single pollutants or short monitoring windows [6,7] . Two seasonal phenomena dominate Cairo's particulate load. In spring, khamsin winds carry Saharan dust into the Nile valley, raising PM10 above 500 μg/m³ during peak events [8] . In autumn, farmers burn rice straw in the Delta, producing a "black cloud" smog that blankets Greater Cairo for weeks [9] . Between these peaks, vehicular and industrial emissions maintain a high baseline. This research aimed to (a) map the spatial distribution of AQI, PM2.5, and PM10 across Greater Cairo over a four-year period, (b) identify seasonal patterns and their meteorological drivers, and (c) apportion PM2.5 sources using a positive matrix factorisation (PMF) receptor model. All data processing and modelling were carried out at the Cartagena Institute of Climate and Environment, Bolívar, Colombia, using datasets shared by the Egyptian Environmental Affairs Agency.

Model Description and Parameterisation

Source apportionment was performed with EPA PMF version 5.0, a multivariate receptor model that decomposes measured concentrations into source profiles and contributions without requiring a priori emission inventories [10] . The model solves X = GF + E, where X is the concentration matrix, G the source contribution matrix, F the source profile matrix, and E the residual. Input data comprised 384 PM2.5 filter samples analysed for 18 species: EC, OC, sulphate, nitrate, ammonium, and 13 trace elements. Uncertainty estimates followed Polissar et al. [11] . The model was tested with 4-8 factors and 100 bootstrap iterations; a six-factor solution was selected based on the lowest Q-robust/Q-expected ratio and physical interpretability.

Materials and Methods

Materials and Methods

Materials

Air quality data were obtained from 32 monitoring stations operated by the Egyptian Environmental Affairs Agency across Greater Cairo, classified as traffic (n = 12), industrial (n = 8), residential (n = 7), and suburban (n = 5). PM2.5 and PM10 were measured by beta-attenuation monitors (Met One BAM-1022) recording hourly averages. Meteorological variables came from co-located weather stations. Additionally, 384 PM2.5 filter samples were collected at four representative sites for chemical speciation. Monitoring ran from March 2019 to February 2023. Analysis was carried out at the Cartagena Institute of Climate and Environment, Bolívar, Colombia.

Methods

AQI was calculated from daily average PM2.5 and PM10 concentrations using the US EPA breakpoint table, which assigns index values from 0 ("good") to 500 ("hazardous") [5] . When both pollutants exceeded reporting thresholds, the higher sub-index was reported as the composite AQI. Seasonal means were computed for spring (Mar-May), summer (Jun-Aug), autumn (Sep-Nov), and winter (Dec-Feb). Spatial interpolation of annual mean AQI across the metropolitan area used ordinary kriging in ArcGIS 10.8 with a spherical variogram model. Pearson correlation was used to test the association between AQI and distance from the nearest industrial zone. Chemical speciation data fed the PMF receptor model described above. policy level of roughly 42 μg/m³, though direct comparisons must account for differences in monitoring protocols and station placement [1,12] . The dominance of vehicle emissions (34.8% of PM2.5) is consistent with Cairo's traffic densityestimated at over 4.5 million registered vehicles in the governorate, many running on subsidised diesel fuel with weak emission standards [13] .

Results

The two seasonal peaks need different policy responses. The autumn black-cloud episode is amenable to intervention: converting rice straw to compost or bioenergy has been piloted since 2010 but adoption remains low [9,14] . Spring khamsin dust is natural and can only be managed through forecasting and health advisories [8] . Between episodes, traffic and industry keep AQI in the "unhealthy for sensitive groups" band almost year-round. The spatial gradient from industrial to suburban sites (PLI ranging from 182 to 97) confirms that emission proximity remains a strong predictor of exposure, even in a city where regional background levels are already high [15] . This has equity implications: industrial-zone residents, who tend to be lower income, bear a disproportionate pollution burden. Shifting freight transport to ring roads, expanding the Cairo Metro, and enforcing vehicular inspection programmes would address the largest single source. Bootstrap analysis (100 runs) showed all six factors reproduced in over 92% of resamples, and displacement diagnostics found no factor swaps [10,16] . Excluding one site at a time changed source estimates by less than±3.1%.

Conclusion

This research mapped the AQI and particulate matter distribution across Greater Cairo over four years using data from 32 monitoring stations. Annual mean PM2.5 ranged from 47 μg/m³ at suburban locations to 129 μg/m³ near the Helwan industrial belt-three to eight times the WHO guideline. AQI exceeded the "unhealthy" threshold on more than 40% of monitored days. Two seasonal peaks-autumn rice-straw burning and spring khamsin dust-superimposed onto a high vehicular and industrial baseline. PMF source apportionment identified vehicle emissions as the largest PM2.5 contributor (34.8%), followed by industry and open burning. The strong negative correlation between AQI and distance from industrial zones highlights that exposure is spatially uneven and concentrated in lower-income districts. Practical steps to improve air quality include enforcing vehicular emission standards, expanding alternatives to ricestraw burning, and relocating heavy freight traffic away from residential corridors. Future work should incorporate personal exposure monitoring and health-outcome data to translate ambient concentrations into quantitative diseaseburden estimates.

Acknowledgement

Acknowledgements Funding SourcesThis research was supported by departmental funds from the Cartagena Institute of Climate and Environment, Bolívar, Colombia. Institutional SupportThe authors thank the Cartagena Institute of Climate and Environment for computational resources and modelling software access.

Figures and tables from the paper

Category	n	PM2.5 (μg/m³)	PM10 (μg/m³)	AQI	Days >150	Days >200
Traffic	12	108.4	231.7	164	187	78
Industrial	8	128.6	267.3	182	214	103
Residential	7	83.1	178.4	139	131	42
Suburban	5	47.3	112.8	97	63	11

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Annual mean PM2.5, PM10, and AQI by station category (2019-2023)

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Season	PM2.5	PM10	AQI	Dominant driver
Spring	94.7	247.1	168	Khamsin dust + traffic
Summer	78.3	164.8	134	Vehicle + industrial baseline
Autumn	112.4	198.6	179	Rice-straw burning + stagnation
Winter	86.9	173.2	147	Inversion trapping + heating

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Table 2Close ×

Seasonal mean AQI and dominant pollution drivers

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Figure 1Close ×

Estimated source contributions to annual mean PM2.5 in Greater Cairo from PMF receptor modelling

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Relationship between annual mean AQI and distance from the nearest industrial zone (n = 32 stations)

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Original paper figureSource PDF · page 3 ↗

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