Evaluation of the Air Quality in Arid Climate Megacities. (Case Study: Greater Cairo)

Evaluation of the Air Quality in Arid Climate Megacities. 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(Case Study: Greater Cairo) Mohammed Mahmoud Hwehy, Fawzia I. Moursy, Attia M. El-Tantawi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3185000/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 31 Mar, 2024 Read the published version in Contributions to Geophysics and Geodesy → Version 1 posted You are reading this latest preprint version Abstract The accelerated urbanization in developing counties in the Middle East and North Africa (MENA) region increases exposure to outdoor air pollution. This work aims to evaluate the ambient air quality in the Greater Cairo area (GC) as one of the largest megacities in the MENA region. The World Health Organization (WHO) classified GC as the largest polluted city in the MENA region. Exploratory data analysis (EDA) was used to assess the pollutants data and meteorological data to show the impacts of weather factors on ambient air quality in the study area. The results show that GC suffers from particle matter (PM) pollutants for both long-term and short-term exposure. The short-term exposure to gaseous pollutants did not exceed the guidelines, however, the long-term did in some traffic areas. The weather and terrain show significant impacts on the temporal and spatial variation of pollutants observations. Most ambient air pollution issues in the MENA region are due to its natural sources and traffic. Air Pollution Terrain Particulate Matter Nitrogen Dioxide Sulfur Dioxide Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction North African countries have the largest desert in the MENA region. It is an arid climate class area dominated by the Sahara. The intertropical Convergence zone's periodical variation around the equator affects rainfall in tropical and subtropical regions [ 1 ]. These facts have impacts on the air quality, such as the nature of pollutants sources, dispersion of pollutants, and dry and wet deposition processes. Air pollution is defined as an imbalance in the chemical composition of the surrounding atmosphere, whether it is due to the difference in the proportions of its components or by the solid, liquid, or gaseous contaminants that are foreign to those components [ 2 ]. This imbalance causes changes in the atmospheric properties including visibility and temperature and mechanisms such as the hydrological cycle. Air pollution causes threats and dangers to human health, while air quality exceeds the WHO’s guideline in more than 90% of the living areas around the world, deaths due to air pollutants are estimated to be 4.2 million annually [ 3 ]. Inhalable and respirable PM with a diameter of 10 microns or less (PM10), including fine PM of 2.5 microns or less (PM2.5) causes health risks, due to its capability of penetrating human lungs and entering the bloodstream [ 4 ]. Nitrogen dioxide (NO 2 ) increases symptoms of bronchitis and asthma, and cardiovascular and respiratory diseases [ 5 ]. NO 2 converts to secondary pollutants including PM2.5 and ground-level Ozone(O 3 ). Sulfur dioxide (SO 2 ) affects the respiratory system and the lungs' function and causes eye irritation. SO 2 combines with atmospheric water content to form acidic rain [ 6 ]. Many studies assess the air quality in the MENA region [ 7 – 12 ]. In most of these previous studies, the WHO’s guideline is used without regard to the effects of the geographical nature of the region, which necessitates that the criterion in assessing air quality is the interim targets issued by the WHO for countries that are encouraged to gradually achieve through enforcing stringent air quality control practices. WHO uses the annual average of airborne PM as an ambient air quality indicator. GC was recorded as one of the most polluted cities in the MENA region during the period from 2011 to 2015; PM10 was greater than 150 µg/m 3 [ 13 ]. The sources of air pollution in GC are natural sources “desert” and anthropogenic sources including vehicle exhaust, open burning of agricultural and municipal wastes, and emissions of industrial facilities located at the planned and unplanned industrial areas spread throughout GC [ 14 – 23 ]. The hourly observations of the ambient air pollution and the meteorological parameter of the study area are important to understand the diurnal variations of air pollution and its trend. The current paper updated the trend of long-term exposure to pollutants in GC and studied the correlation between the elevation of monitoring sites and pollutant concentrations. This paper assesses the recent compatibility of air quality in GC with the WHO's guidelines, to introduce an understanding of the effects of the weather, topography, and daily anthropogenic activities on the air quality in GC. 2. Material and Methods 2.1. Study area: Greater Cairo (GC) refers to Cairo Governorate (the Egyptian capital), and the populated urban and semi-urban areas of the Giza and Qalyubia governorates Fig. 1 [ 24 ]. The climate of GC is hot and dry with a clear sky in summer and moderate winters with little rain [ 25 , 26 ]. This dry and desert climate causes desert nature to be one of the most important sources of total suspended PM pollutants triggered by sandstorms or by stimulating the movement of cars and pedestrians for the accumulated dust on the roads. So, PM averages exceed the levels stipulated by the WHO, and it becomes acceptable for these averages to be compared with the limits of the interim target (IT-1) [ 27 ] to measure the extent of the ability to achieve and adhere to it. 2.2. Data: 2.2.1. Air pollution data: The air pollutants data used are the hourly average data for PM10, NO 2 , and SO 2 , which were obtained from the national network for ambient air quality monitoring, which is owned and managed by the Egyptian Environmental Affairs Agency (EEAA), for the period during January 1, 2010, to December 31, 2019, for the 18 monitoring stations shown in Table 1 . The selected stations achieve operating rates of at least 18 hours daily and 75% of the year days during this period, according to the US EPA [ 28 ]. Table 1 Location, Elevation, and Classification of the Stations Code Area Latitude Longitude Elevation, m Classification GC01 Qaha 30.29 31.21 14 Rural GC02 Abu Zabal 30.25 31.35 22 Rural / Industrial GC03 Shobra 30.11 31.27 13 Residential / Industrial GC04 El-Sahel 30.1 31.24 24 Residential / Traffic GC05 Qullaly 30.06 31.24 27 Traffic GC06 Qasr Aeny 30.03 31.23 27 Traffic GC07 Heliopolis 30.11 31.34 48 Residential / Traffic GC08 Abbasia 30.08 31.29 29 Residential / Traffic GC09 Nasr City 30.06 31.33 94 Residential / Traffic GC10 New Cairo 29.99 31.42 268 Residential GC11 Sallam 30.16 31.45 67 Traffic GC12 Giza Square 30.02 31.21 23 Residential / Traffic GC13 Mohandisin 30.05 31.21 37 Traffic GC14 6th October 29.94 30.88 137 Industrial GC15 Maadi 29.97 31.26 29 Residential / Traffic GC16 Massara 29.91 31.3 25 Residential / Industrial GC17 Helwan 29.87 31.32 45 Residential / Industrial GC18 Tibben 29.78 31.3 21 Industrial Table 1 Figure 1 2.2.2. Meteorological data: The effects of weather conditions on the air quality for the GC region were assessed by using the meteorological variables including wind speed (WS, m/s), wind direction (WD as degree), temperature (T°C), and the recorded weather phenomena (sand (SA), sandstorm (SS), dust (DU), haze (Hz)). The Weather observations data for Cairo airport station (WMO code: HECA) were obtained for a similar duration of the pollution data from the Iowa State University - Iowa Environmental Mesonota database of archives of automated airport weather observations Automated Surface Observing System 'ASOS Metar' and the Integrated Surface Dataset from NOAA National Center for Environmental Information NCEI [ 29 , 30 ]. The fifth-generation mesoscale model (MM5), developed by the National Center for Atmospheric Research at Pennsylvania State University [ 31 ], was used to predict the height of the planetary boundary layer HPBL, which is an indicator of the atmospheric mixing layer height. Most monitoring stations were located at the same height above the ground level, approximately 10 meters, so only the elevation of the monitoring site was evaluated. The shuttle radar topography mission (SRTM) elevation data at a resolution of 30 meters, obtained from the United States Geological Survey (USGS)'s center of earth resources observation and science (EROS)[ 32 ] was used. 3. Methodology The statistical analysis explored the data, including measures of central tendency and dispersion for the raw data obtained from the EEAA. The daily and annual averages of pollutant concentrations were calculated after excluding the daily observations of less than 75% of the hours. The frequencies of meteorological and weather phenomena and pollutant concentrations were calculated. The combined arithmetic average (Eq. 1) [ 33 ] was used to calculate the weighted mean of separated data sets of pollutant concentrations in the study area for hours, days, and months to understand the temporal variation of the influence of human activity and the atmospheric temperatures in the study area on the pollutants pattern. To clarify the pattern of different pollutants in the various analyzed referred, range scaling of results using the minimum and maximum method was used. \(\text{C}\text{o}\text{m}\text{b}\text{i}\text{n}\text{e}\text{d} \text{a}\text{r}\text{i}\text{t}\text{h}\text{m}\text{e}\text{t}\text{i}\text{c} \text{a}\text{v}\text{e}\text{r}\text{a}\text{g}\text{e}, \stackrel{-}{X} = \frac{\sum _{i=1}^{s}{n}_{i,j} \stackrel{-}{{x}_{i,j}}}{\sum _{i=1}^{s}{n}_{i,j}}\) (Eq. 1) Where \(\stackrel{-}{\mathbf{X}}\) is the Combined mean, \({\stackrel{-}{\varvec{x}}}_{\varvec{i},\varvec{j}}\) is the mean of the i th station for a j th pollutant, \({\mathbf{n}}_{\mathbf{i},\mathbf{j}}\) is the sample size of the i th station for a j th pollutant, and s is the total count of stations (s = 18). Simple linear regression explained the basic trend for each pollutant at each station. Hierarchical Cluster Analysis HCA [ 34 ] was employed as a part of the tool kit for exploratory data analysis. HCA is an unsupervised multivariate analysis to identify groups of similar characteristics. Euclidean distance metric described dissimilarity and measured the distance between multidimensional data. For (m) variables, the Euclidean distance was given by the multidimensional Pythagorean equation (Eq. 2) for all pairs of objects defined by the standardized variables. \(\text{E}\text{u}\text{c}\text{l}\text{i}\text{d}\text{e}\text{a}\text{n} \text{d}\text{i}\text{s}\text{t}\text{a}\text{n}\text{c}\text{e}, d = \sqrt{{\sum }_{j=1}^{m}{\left({xs}_{i,j}-{xs}_{2,j}\right)}^{2}}\) (Eq. 2) \(\text{S}\text{t}\text{a}\text{n}\text{d}\text{a}\text{r}\text{d}\text{i}\text{z}\text{e}\text{d} \text{v}\text{a}\text{l}\text{u}\text{e},{xs}_{i,j}= \frac{{x}_{i,j} - {\stackrel{-}{x}}_{i,j}}{{sd}_{i,j}}\) (Eq. 3) 3. Results and discussion 4.1. Description of meteorological data: Meteorological data, in supplemental file, showed that the northern wind is the prevailing wind direction. The calm to light wind speed, which is suitable for haze formation, prevailed during the early morning hours. During sunshine hours, mild and moderate winds prevail. Active winds and storms often appear with the advancement of daylight hours. It was also found that the highest frequency of calm winds is from November to February, and the lowest is during the summer months. Active and stormy winds are highest during the winter and spring months, and the lowest is otherwise. The winds associated with the recorded observations of dust and sand showed that the prevailing winds during storms are Southwest, South, and West, respectively, and the frequencies of wind higher than 5 m/s exceed 86%. Haze has a higher frequency during the night than during the daytime. Also, the highest values are at the onset of human activity in the morning, but it decreases with the temperature increases. As for the events of dust and sand, their frequency often increases with the progression of daylight hours. Human activities are the source of the haze that appears in the atmosphere and affects visibility before it is cleared by natural factors [ 35 ]. The frequency of haze during the weekdays illustrated that vehicle exhausts have the greatest effect, as higher values show it with traffic density hours in GC [ 36 ], which are related to working hours. It also shows the lowest frequencies during the weekend on Fridays. And there appears a pattern extended during Thursday afternoon, which may explain as high traffic due to most of the non-indigenous residents traveling to neighboring cities to spend their weekends, in contrast to an extended pattern during Saturday, which is the day of the return of travelers. 4.2. Description of ambient air monitoring data: Exploratory data analysis (EDA) was applied to the hourly data obtained from EEAA and to the calculated daily average. Table 2 lists the WHO air quality guideline (interim target 1 for PM10) and the regulatory standards of the Egyptian Law of Environment (EEAA’s executive regulation) [ 37 ]. The box plot, which is one of the EDA techniques used for data summarization and shown in Fig. 2 , clears those guidelines of WHO and EEAA were achieved for both NO 2 and SO 2 whether for the hourly and the daily averages as short-term exposure to gases pollutants. Oppositely for the daily averages of the PM10, the results showed that the standards were exceeded in varying proportions between the studying stations, where the GC2, GC5, and GC16 stations have high median than 150 µg/m 3 , on the other hand, the third quartile for the stations GC8, GC9, GC10 and, GC12 are less than 150 µg/m 3 . That highly agrees with the results of Wheida et al., [ 22 ] in their study of pollutants in GC for the duration begging from 2010 to 2015. Table 2 The WHO's interim target − 1 and the Egyptian Environmental regulator of Air Quality Standards Pollutant, unit Period Area classification WHO, IT-1 EEAA PM10 µg/m 3 Daily - 150 150 Annual - 70 70 SO2 µg/m 3 Hourly Urban 500 300 Industrial 350 Daily Urban 125 125 Industrial 150 Annual Urban 20 50 Industrial 60 NO2 µg/m 3 Hourly - 200 300 Daily - - 150 Annual Urban 40 60 Industrial 80 Table 2 Figure 2 4.3. Spatial variation of daily averages of pollutants: The daily average concentrations of pollutants as a short-term exposure show an inverse correlation to the station’s elevation (-0.62, -0.25, -0.16) for PM10, SO 2, and NO 2 , respectively. Figure 3 shows that the most of stations, which have the smallest height MSL, exceeded the PM10’s daily standard of more than 25% of the actual operation days during the study period. The largest results were for stations GC02, GC05, and GC06 where the averages were 203 ± 42, 196 ± 40, and 196 ± 31 respectively. Otherwise, the stations, have the highest elevation and are located at the Mokattam hill (southeastern of the Nile Delta) and Al-Haram highland (southwest of the Nile Delta). Station GC10 had the smallest average of the daily averages calculated (Mean = 96, SD = 85). The inverse correlation between the station's elevation and pollutants concentration confirms that pollution is affected by topographic characteristics [ 14 ]. Figure 3 Table 3 shows the annual averages of pollutants concentration as long-term exposure, the PM10 exceeded WHO’s guidelines and EEAA’s standards. Though both SO 2 and NO 2 did not exceed the WHO’s guideline or EEAA’s standard for short-term exposure, the high traffic areas located at mid of the Nile basin, exceeded the long-term exposure standards. The annual average of NO 2 exceeded the WHO’s guideline in almost all traffic and industrial areas, especially the center of the city which exceeded EEAA’s standards. This was less stressful for SO 2 , as it exceeded the WHO’s guidelines in some stations, especially those located in traffic areas due to the sulfur content in gasoline fuel used in vehicles. The basic trends of pollutants detected for each station showed that negative trend of PM10 annual means for most of the stations except for high-traffic stations GC07 and GC11 shown a positive trend. Also, industrial and traffic stations showed a positive trend for gas pollutants. The negative trend of GC01 station, which indicates the agricultural activities, proves the success of the efforts of Egyptians to prevent uncontrolled biomass burning. Table 3 Averages of Annual means of pollutants, standard deviation, and slop of simple linear regression of each pollutant and time Station PM10 NO2 SO2 Mean SD Slope Mean SD Slope Mean SD Slope GC01 161 41 -12 30 13 0 13 6 1 GC02 203 42 -10 25 11 2 14 5 1 GC03 163 49 -9 25 16 2 11 4 -1 GC04 148 33 -6 25 8 0 15 6 1 GC05 193 40 -6 63 24 6 21 5 1 GC06 152 37 -8 36 11 -1 18 8 1 GC07 178 25 5 33 14 0 14 6 1 GC08 116 23 -3 30 10 1 14 7 0 GC09 105 18 -3 34 10 2 12 4 1 GC10 97 10 -1 25 7 1 11 4 0 GC11 149 18 3 24 10 3 9 3 1 GC12 129 27 -6 37 12 2 14 6 1 GC13 165 31 -9 38 13 3 18 11 3 GC14 134 31 -5 27 9 2 12 6 1 GC15 163 46 -10 26 9 0 11 4 0 GC16 196 31 -4 24 10 2 10 3 1 GC17 149 32 -5 32 14 0 12 4 0 GC18 166 14 0 20 5 0 10 3 1 Table 3 The hierarchical cluster analysis, whether average or complete methods for calculating dissimilarity, used to explore the spatial variability of ambient air pollutant monitoring data indicated that the air quality in GC can be classified into three main clusters as shown in Dendrogram in Fig. 4 . The first cluster includes GC10 and GC14, both were the lowest in the pollutant’s concentration, respectively. Although the two stations are in different areas (GC10 residential areas and GC14 industrial areas), this cluster represents the effect of elevation. The second cluster consists of GC05, which was the highest pollutant exceeding standards. The third cluster includes the rest of the stations located between the two previous classifications. This indicated that traffic areas are the highest concentration of pollutants, then industrial areas, mixed areas, and finally residential areas. The concentration of pollutants gradually decreases with elevation (MSL) increasing. Figure 4 4.4. Temporal Variation of Pollutants Data: Figure 5 shows the Combined arithmetic averages, which are calculated for the hourly and daily averages. It showed that the variation in the levels of pollutants during the hours or the days was related to human activities. Where pollutants begin to increase with the onset of human activity and are at their lowest levels during the hours of the night or with the rest of the residents of GC. PM10 and NO 2 have two peaks during the day, which meet the two peaks of traffic density [ 18 , 36 ]. It is also clear that the lowest level of all pollutants was Friday, which is the weekly holiday in Egypt. The most important observation was the refraction of pollutant levels with the increase in the maximum temperatures during the day, which indicates the effect of increasing the mixing layer height below the planetary boundary layer as well as the increase in wind speeds on the dispersion of pollutants in GC. The variation in the level of the combined arithmetic averages for the months during the year confirmed that the level of pollutants is at its lowest levels during the summer months, which indicates the effect of increasing the planetary boundary layer height and increasing the average temperatures, which has an impact on the improvement of air quality in GC. Figure 5 4. Conclusion Exploratory data analysis (EDA) of pollutants in GC clearly showed that the issue of air pollution is due to the problem of increasing particulate matter concentrations, whether in short-term exposure or long-term exposure. As for gaseous pollutants, they did not exceed the guidelines of WHO or the regulations of the Egyptian Environmental Law for short-term exposure, and some violations were recorded in areas with high traffic or heavy industrial activity for long-term exposure. It turns out that sandstorms in GC are associated with gusty southern winds higher than 5 m/s. And its frequency is significantly higher with advancement in the hours of the day than during the night. On the contrary, the haze occurs during calm winds of less than 3 m/s, and their highest frequency is during the early morning hours, and gradually fades with progress in the daylight hours. In general, weather factors help disperse local pollutants during daylight hours due to moderate winds and increasing mixing layer height. While the weather factors help the concentration of pollutants during the early morning and evening hours, due to the calm winds and the decreasing mixing layer height. As for the annual pattern, the weather factors help to disperse local pollutants during the summer months due to wind speeds are higher than in the winter months, and the mean mixing layer height increases due to the mean temperature increases. The effect of the terrain is also evident in the dispersal of local pollutants, as there was a positive correlation between the elevation of the monitoring sites and pollutant averages. This caused stations located in the Nile River basin to have higher concentrations. This suggests that the sources of pollution in GC are either natural sources as a result of raising the accumulated dust with the wind blowing, or as a result of traffic congestion and large, planned, or small and craft industrial activities. Recent results indicate the successful control of the open burning of agricultural residues and biomass due to the efforts of the Egyptian Ministry of Environment in confronting acute air pollution episodes. Declarations Funding: The authors declare that this work has not received any financial support (No funds, grants, or other support was received). Conflicts of interest/Competing interests : The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Availability of data : The raw data analyzed during the present study are available from the Egyptian Environmental Affairs Agency on reasonable request. Code availability (software application or custom code): Not applicable. Authors' contributions : Mohammed Mahmoud Hwehy: Conceptualization Ideas, Methodology Development, Validation, Formal analysis, Resources Provision of study, Visualization Preparation, and Writing - Original Draft Preparation Fawzia I. Moursy: Supervision, Methodology Development, and Writing - Review Attia M. El-Tantawi: Supervision, Methodology Development, and Writing - Review Mostafa A. Mohamed: Conceptualization Ideas, Data Curation, Validation, and Writing - Review Ethics approval : Not applicable. Consent to participate : Not applicable. 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Ali SA, Tamura A. Road traffic noise mitigation strategies in Greater Cairo, Egypt. Applied Acoustics 2002;63:1257–65. https://doi.org/10.1016/S0003-682X(02)00046-4. EEAA. Egyptian Law for the Protection of the Environment number 4/1994 Amended by Law number 9/2009 and its Executive Regulation 2009. http://www.eeaa.gov.eg/en-us/laws/envlaw.aspx (accessed March 19, 2021). Additional Declarations No competing interests reported. Supplementary Files METSupplmentfile.xlsx Cite Share Download PDF Status: Published Journal Publication published 31 Mar, 2024 Read the published version in Contributions to Geophysics and Geodesy → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3185000","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":223462553,"identity":"b6ef6fc1-a526-4369-a499-a9fbaa8434e0","order_by":0,"name":"Mohammed Mahmoud Hwehy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYFACHjDJ2CDBwMDMwGADYjceIEVLGphNkpbDYB5eLbrtvQc/3cyxk22Q7jF8XNh23m5t+2GgLTU20bi0mJ05lyyduy3ZuEHmjLHxzLbbydvOJAK1HEvLbcCl5UaOAVALc2KDRO42aV6gFrMDQC2MDYfxaTH+nbutHqRl+2/etnPJZucfEtRiBrTlMNgWZt62A3ZmNwjZcuaMmXXutuPGbRL5n6V5ziUnmN0A2pKAzy/He4xv526rlu2XSEv8zFNmZ292Pv3hgw81Nji1wAEbiGBkY0gEq0wgpBwB/jDYE694FIyCUTAKRgoAALrKZV71h/DsAAAAAElFTkSuQmCC","orcid":"","institution":"Egyptian Environmental Affairs Agency EEAA","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"Mahmoud","lastName":"Hwehy","suffix":""},{"id":223462554,"identity":"62761801-7f43-45f3-aa1c-2185ebe850cd","order_by":1,"name":"Fawzia I. Moursy","email":"","orcid":"","institution":"Cairo University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fawzia","middleName":"I.","lastName":"Moursy","suffix":""},{"id":223462555,"identity":"91ec3e3c-1b97-4af1-9ae9-f3948cb4b464","order_by":2,"name":"Attia M. El-Tantawi","email":"","orcid":"","institution":"Cairo University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Attia","middleName":"M.","lastName":"El-Tantawi","suffix":""},{"id":223462556,"identity":"99005eea-e497-4109-a04b-ea9a78250147","order_by":3,"name":"Mostafa A. Mohamed","email":"","orcid":"","institution":"Cairo University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mostafa","middleName":"A.","lastName":"Mohamed","suffix":""}],"badges":[],"createdAt":"2023-07-19 12:14:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3185000/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3185000/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.31577/congeo.2024.54.1.6","type":"published","date":"2024-04-01T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":41251319,"identity":"8069e472-fb47-4a19-9181-0ebe001e4178","added_by":"auto","created_at":"2023-08-08 15:10:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":313572,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Area Maps; (A) Africa (black dashes) and MENA (red dashes) map, (B) GC LULC, and (C) AAQ Stations.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3185000/v1/e6b785cc8e08430bb72f582a.png"},{"id":41252648,"identity":"da817369-8160-442e-b7f5-b5b80a26f87a","added_by":"auto","created_at":"2023-08-08 15:18:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":23920,"visible":true,"origin":"","legend":"\u003cp\u003eBox Plot of Hourly and Daily averages of pollutants\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3185000/v1/6518c36e1174934f23c15a40.png"},{"id":41251321,"identity":"982b4bb1-6914-4279-a536-81376038a783","added_by":"auto","created_at":"2023-08-08 15:10:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1069532,"visible":true,"origin":"","legend":"\u003cp\u003eThe percentage of daily averages of the PM10 agree with the indications of the WHO and the Egyptian Environmental regulations\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3185000/v1/edf1c55403af5b92aa75bb5f.png"},{"id":41251322,"identity":"696191e7-d827-48ee-bd35-3d05e8050ff3","added_by":"auto","created_at":"2023-08-08 15:10:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":384459,"visible":true,"origin":"","legend":"\u003cp\u003eDendrogram\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-3185000/v1/a567d9fedb01167ad79d3f3d.png"},{"id":41251323,"identity":"4c1bf04e-c438-4b6f-9a1b-67e78d95893e","added_by":"auto","created_at":"2023-08-08 15:10:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":659907,"visible":true,"origin":"","legend":"\u003cp\u003eVariation pattern of the Pollutants (a) Hourly, (b) weekdays, and (c) Months\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3185000/v1/3d944993da0fcd9bd7679108.png"},{"id":54529447,"identity":"2c5f4560-995b-42d7-921e-15d342d57e92","added_by":"auto","created_at":"2024-04-11 23:28:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2232988,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3185000/v1/e3b60bd2-9df8-4711-96e7-59a95ab33e98.pdf"},{"id":41251324,"identity":"6918f76f-77e7-4e22-990b-933ae7dad86b","added_by":"auto","created_at":"2023-08-08 15:10:03","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":5567113,"visible":true,"origin":"","legend":"","description":"","filename":"METSupplmentfile.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3185000/v1/ba21948d5e01c6a7911eb306.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of the Air Quality in Arid Climate Megacities. (Case Study: Greater Cairo)","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNorth African countries have the largest desert in the MENA region. It is an arid climate class area dominated by the Sahara. The intertropical Convergence zone's periodical variation around the equator affects rainfall in tropical and subtropical regions [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These facts have impacts on the air quality, such as the nature of pollutants sources, dispersion of pollutants, and dry and wet deposition processes.\u003c/p\u003e \u003cp\u003eAir pollution is defined as an imbalance in the chemical composition of the surrounding atmosphere, whether it is due to the difference in the proportions of its components or by the solid, liquid, or gaseous contaminants that are foreign to those components [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This imbalance causes changes in the atmospheric properties including visibility and temperature and mechanisms such as the hydrological cycle. Air pollution causes threats and dangers to human health, while air quality exceeds the WHO\u0026rsquo;s guideline in more than 90% of the living areas around the world, deaths due to air pollutants are estimated to be 4.2\u0026nbsp;million annually [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInhalable and respirable PM with a diameter of 10 microns or less (PM10), including fine PM of 2.5 microns or less (PM2.5) causes health risks, due to its capability of penetrating human lungs and entering the bloodstream [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e) increases symptoms of bronchitis and asthma, and cardiovascular and respiratory diseases [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. NO\u003csub\u003e2\u003c/sub\u003e converts to secondary pollutants including PM2.5 and ground-level Ozone(O\u003csub\u003e3\u003c/sub\u003e). Sulfur dioxide (SO\u003csub\u003e2\u003c/sub\u003e) affects the respiratory system and the lungs' function and causes eye irritation. SO\u003csub\u003e2\u003c/sub\u003e combines with atmospheric water content to form acidic rain [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMany studies assess the air quality in the MENA region [\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In most of these previous studies, the WHO\u0026rsquo;s guideline is used without regard to the effects of the geographical nature of the region, which necessitates that the criterion in assessing air quality is the interim targets issued by the WHO for countries that are encouraged to gradually achieve through enforcing stringent air quality control practices. WHO uses the annual average of airborne PM as an ambient air quality indicator. GC was recorded as one of the most polluted cities in the MENA region during the period from 2011 to 2015; PM10 was greater than 150 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe sources of air pollution in GC are natural sources \u0026ldquo;desert\u0026rdquo; and anthropogenic sources including vehicle exhaust, open burning of agricultural and municipal wastes, and emissions of industrial facilities located at the planned and unplanned industrial areas spread throughout GC [\u003cspan additionalcitationids=\"CR15 CR16 CR17 CR18 CR19 CR20 CR21 CR22\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe hourly observations of the ambient air pollution and the meteorological parameter of the study area are important to understand the diurnal variations of air pollution and its trend. The current paper updated the trend of long-term exposure to pollutants in GC and studied the correlation between the elevation of monitoring sites and pollutant concentrations. This paper assesses the recent compatibility of air quality in GC with the WHO's guidelines, to introduce an understanding of the effects of the weather, topography, and daily anthropogenic activities on the air quality in GC.\u003c/p\u003e"},{"header":"2. Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Study area:\u003c/h2\u003e\n \u003cp\u003eGreater Cairo (GC) refers to Cairo Governorate (the Egyptian capital), and the populated urban and semi-urban areas of the Giza and Qalyubia governorates Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. The climate of GC is hot and dry with a clear sky in summer and moderate winters with little rain [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. This dry and desert climate causes desert nature to be one of the most important sources of total suspended PM pollutants triggered by sandstorms or by stimulating the movement of cars and pedestrians for the accumulated dust on the roads. So, PM averages exceed the levels stipulated by the WHO, and it becomes acceptable for these averages to be compared with the limits of the interim target (IT-1) [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e] to measure the extent of the ability to achieve and adhere to it.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2. Data:\u003c/h2\u003e\n \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.1. Air pollution data:\u003c/h2\u003e\n \u003cp\u003eThe air pollutants data used are the hourly average data for PM10, NO\u003csub\u003e2\u003c/sub\u003e, and SO\u003csub\u003e2\u003c/sub\u003e, which were obtained from the national network for ambient air quality monitoring, which is owned and managed by the Egyptian Environmental Affairs Agency (EEAA), for the period during January 1, 2010, to December 31, 2019, for the 18 monitoring stations shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The selected stations achieve operating rates of at least 18 hours daily and 75% of the year days during this period, according to the US EPA [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e].\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLocation, Elevation, and Classification of the Stations\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCode\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLatitude\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLongitude\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eElevation, m\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eClassification\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQaha\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbu Zabal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRural / Industrial\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShobra\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidential / Industrial\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEl-Sahel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidential / Traffic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQullaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTraffic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQasr Aeny\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTraffic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeliopolis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidential / Traffic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbbasia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidential / Traffic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNasr City\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidential / Traffic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNew Cairo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidential\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSallam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTraffic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGiza Square\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidential / Traffic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMohandisin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTraffic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6th October\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndustrial\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaadi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidential / Traffic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMassara\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidential / Industrial\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHelwan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResidential / Industrial\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTibben\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndustrial\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.2. Meteorological data:\u003c/h2\u003e\n \u003cp\u003eThe effects of weather conditions on the air quality for the GC region were assessed by using the meteorological variables including wind speed (WS, m/s), wind direction (WD as degree), temperature (T\u0026deg;C), and the recorded weather phenomena (sand (SA), sandstorm (SS), dust (DU), haze (Hz)). The Weather observations data for Cairo airport station (WMO code: HECA) were obtained for a similar duration of the pollution data from the Iowa State University - Iowa Environmental Mesonota database of archives of automated airport weather observations Automated Surface Observing System \u0026apos;ASOS Metar\u0026apos; and the Integrated Surface Dataset from NOAA National Center for Environmental Information NCEI [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]. The fifth-generation mesoscale model (MM5), developed by the National Center for Atmospheric Research at Pennsylvania State University [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e], was used to predict the height of the planetary boundary layer HPBL, which is an indicator of the atmospheric mixing layer height. Most monitoring stations were located at the same height above the ground level, approximately 10 meters, so only the elevation of the monitoring site was evaluated. The shuttle radar topography mission (SRTM) elevation data at a resolution of 30 meters, obtained from the United States Geological Survey (USGS)\u0026apos;s center of earth resources observation and science (EROS)[\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e] was used.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003ch3\u003e3. Methodology\u003c/h3\u003e\n\u003cp\u003eThe statistical analysis explored the data, including measures of central tendency and dispersion for the raw data obtained from the EEAA. The daily and annual averages of pollutant concentrations were calculated after excluding the daily observations of less than 75% of the hours. The frequencies of meteorological and weather phenomena and pollutant concentrations were calculated. The combined arithmetic average (Eq.\u0026nbsp;1) [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e] was used to calculate the weighted mean of separated data sets of pollutant concentrations in the study area for hours, days, and months to understand the temporal variation of the influence of human activity and the atmospheric temperatures in the study area on the pollutants pattern. To clarify the pattern of different pollutants in the various analyzed referred, range scaling of results using the minimum and maximum method was used.\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\text{C}\\text{o}\\text{m}\\text{b}\\text{i}\\text{n}\\text{e}\\text{d} \\text{a}\\text{r}\\text{i}\\text{t}\\text{h}\\text{m}\\text{e}\\text{t}\\text{i}\\text{c} \\text{a}\\text{v}\\text{e}\\text{r}\\text{a}\\text{g}\\text{e}, \\stackrel{-}{X} = \\frac{\\sum _{i=1}^{s}{n}_{i,j} \\stackrel{-}{{x}_{i,j}}}{\\sum _{i=1}^{s}{n}_{i,j}}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e(Eq.\u0026nbsp;1)\u003c/p\u003e\n\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\stackrel{-}{\\mathbf{X}}\\)\u003c/span\u003e\u003c/span\u003eis the Combined mean, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\stackrel{-}{\\varvec{x}}}_{\\varvec{i},\\varvec{j}}\\)\u003c/span\u003e\u003c/span\u003e is the mean of the \u003cem\u003ei\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e station for a \u003cem\u003ej\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e pollutant, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\mathbf{n}}_{\\mathbf{i},\\mathbf{j}}\\)\u003c/span\u003e\u003c/span\u003eis the sample size of the \u003cem\u003ei\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e station for a \u003cem\u003ej\u003c/em\u003e\u003csup\u003e\u003cem\u003eth\u003c/em\u003e\u003c/sup\u003e pollutant, and s is the total count of stations (s\u0026thinsp;=\u0026thinsp;18).\u003c/p\u003e\n\u003cp\u003eSimple linear regression explained the basic trend for each pollutant at each station. Hierarchical Cluster Analysis HCA [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e] was employed as a part of the tool kit for exploratory data analysis. HCA is an unsupervised multivariate analysis to identify groups of similar characteristics. Euclidean distance metric described dissimilarity and measured the distance between multidimensional data. For (m) variables, the Euclidean distance was given by the multidimensional Pythagorean equation (Eq.\u0026nbsp;2) for all pairs of objects defined by the standardized variables.\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\text{E}\\text{u}\\text{c}\\text{l}\\text{i}\\text{d}\\text{e}\\text{a}\\text{n} \\text{d}\\text{i}\\text{s}\\text{t}\\text{a}\\text{n}\\text{c}\\text{e}, d = \\sqrt{{\\sum }_{j=1}^{m}{\\left({xs}_{i,j}-{xs}_{2,j}\\right)}^{2}}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e (Eq. 2)\u003c/p\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\text{S}\\text{t}\\text{a}\\text{n}\\text{d}\\text{a}\\text{r}\\text{d}\\text{i}\\text{z}\\text{e}\\text{d} \\text{v}\\text{a}\\text{l}\\text{u}\\text{e},{xs}_{i,j}= \\frac{{x}_{i,j} - {\\stackrel{-}{x}}_{i,j}}{{sd}_{i,j}}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e (Eq. 3)\u003c/p\u003e"},{"header":"3. Results and discussion","content":"\u003ch2\u003e4.1. Description of meteorological data:\u003c/h2\u003e\n\u003cp\u003eMeteorological data, in supplemental file, showed that the northern wind is the prevailing wind direction. The calm to light wind speed, which is suitable for haze formation, prevailed during the early morning hours. During sunshine hours, mild and moderate winds prevail. Active winds and storms often appear with the advancement of daylight hours. It was also found that the highest frequency of calm winds is from November to February, and the lowest is during the summer months. Active and stormy winds are highest during the winter and spring months, and the lowest is otherwise. The winds associated with the recorded observations of dust and sand showed that the prevailing winds during storms are Southwest, South, and West, respectively, and the frequencies of wind higher than 5 m/s exceed 86%. Haze has a higher frequency during the night than during the daytime. Also, the highest values are at the onset of human activity in the morning, but it decreases with the temperature increases. As for the events of dust and sand, their frequency often increases with the progression of daylight hours. Human activities are the source of the haze that appears in the atmosphere and affects visibility before it is cleared by natural factors [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]. The frequency of haze during the weekdays illustrated that vehicle exhausts have the greatest effect, as higher values show it with traffic density hours in GC [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e], which are related to working hours. It also shows the lowest frequencies during the weekend on Fridays. And there appears a pattern extended during Thursday afternoon, which may explain as high traffic due to most of the non-indigenous residents traveling to neighboring cities to spend their weekends, in contrast to an extended pattern during Saturday, which is the day of the return of travelers.\u003c/p\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2. Description of ambient air monitoring data:\u003c/h2\u003e\n \u003cp\u003eExploratory data analysis (EDA) was applied to the hourly data obtained from EEAA and to the calculated daily average. Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e lists the WHO air quality guideline (interim target 1 for PM10) and the regulatory standards of the Egyptian Law of Environment (EEAA\u0026rsquo;s executive regulation) [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. The box plot, which is one of the EDA techniques used for data summarization and shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, clears those guidelines of WHO and EEAA were achieved for both NO\u003csub\u003e2\u003c/sub\u003e and SO\u003csub\u003e2\u003c/sub\u003e whether for the hourly and the daily averages as short-term exposure to gases pollutants. Oppositely for the daily averages of the PM10, the results showed that the standards were exceeded in varying proportions between the studying stations, where the GC2, GC5, and GC16 stations have high median than 150 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, on the other hand, the third quartile for the stations GC8, GC9, GC10 and, GC12 are less than 150 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e. That highly agrees with the results of Wheida et al., [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e] in their study of pollutants in GC for the duration begging from 2010 to 2015.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe WHO\u0026apos;s interim target \u0026minus;\u0026thinsp;1 and the Egyptian Environmental regulator of Air Quality Standards\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePollutant, unit\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePeriod\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eArea classification\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWHO, IT-1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEEAA\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePM10 \u0026micro;g/m\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDaily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnnual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eSO2 \u0026micro;g/m\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eHourly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndustrial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e350\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDaily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndustrial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAnnual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndustrial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eNO2 \u0026micro;g/m\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHourly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDaily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eAnnual\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndustrial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3. Spatial variation of daily averages of pollutants:\u003c/h2\u003e\n \u003cp\u003eThe daily average concentrations of pollutants as a short-term exposure show an inverse correlation to the station\u0026rsquo;s elevation (-0.62, -0.25, -0.16) for PM10, SO\u003csub\u003e2,\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e, respectively. Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows that the most of stations, which have the smallest height MSL, exceeded the PM10\u0026rsquo;s daily standard of more than 25% of the actual operation days during the study period. The largest results were for stations GC02, GC05, and GC06 where the averages were 203\u0026thinsp;\u0026plusmn;\u0026thinsp;42, 196\u0026thinsp;\u0026plusmn;\u0026thinsp;40, and 196\u0026thinsp;\u0026plusmn;\u0026thinsp;31 respectively. Otherwise, the stations, have the highest elevation and are located at the Mokattam hill (southeastern of the Nile Delta) and Al-Haram highland (southwest of the Nile Delta). Station GC10 had the smallest average of the daily averages calculated (Mean\u0026thinsp;=\u0026thinsp;96, SD\u0026thinsp;=\u0026thinsp;85). The inverse correlation between the station\u0026apos;s elevation and pollutants concentration confirms that pollution is affected by topographic characteristics [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the annual averages of pollutants concentration as long-term exposure, the PM10 exceeded WHO\u0026rsquo;s guidelines and EEAA\u0026rsquo;s standards. Though both SO\u003csub\u003e2\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e did not exceed the WHO\u0026rsquo;s guideline or EEAA\u0026rsquo;s standard for short-term exposure, the high traffic areas located at mid of the Nile basin, exceeded the long-term exposure standards. The annual average of NO\u003csub\u003e2\u003c/sub\u003e exceeded the WHO\u0026rsquo;s guideline in almost all traffic and industrial areas, especially the center of the city which exceeded EEAA\u0026rsquo;s standards. This was less stressful for SO\u003csub\u003e2\u003c/sub\u003e, as it exceeded the WHO\u0026rsquo;s guidelines in some stations, especially those located in traffic areas due to the sulfur content in gasoline fuel used in vehicles. The basic trends of pollutants detected for each station showed that negative trend of PM10 annual means for most of the stations except for high-traffic stations GC07 and GC11 shown a positive trend. Also, industrial and traffic stations showed a positive trend for gas pollutants. The negative trend of GC01 station, which indicates the agricultural activities, proves the success of the efforts of Egyptians to prevent uncontrolled biomass burning.\u0026nbsp;\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAverages of Annual means of pollutants, standard deviation, and slop of simple linear regression of each pollutant and time\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eStation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ePM10\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eNO2\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eSO2\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSlope\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSlope\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSlope\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e149\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGC18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eThe hierarchical cluster analysis, whether average or complete methods for calculating dissimilarity, used to explore the spatial variability of ambient air pollutant monitoring data indicated that the air quality in GC can be classified into three main clusters as shown in Dendrogram in Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e. The first cluster includes GC10 and GC14, both were the lowest in the pollutant\u0026rsquo;s concentration, respectively. Although the two stations are in different areas (GC10 residential areas and GC14 industrial areas), this cluster represents the effect of elevation. The second cluster consists of GC05, which was the highest pollutant exceeding standards. The third cluster includes the rest of the stations located between the two previous classifications. This indicated that traffic areas are the highest concentration of pollutants, then industrial areas, mixed areas, and finally residential areas. The concentration of pollutants gradually decreases with elevation (MSL) increasing.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4. Temporal Variation of Pollutants Data:\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e shows the Combined arithmetic averages, which are calculated for the hourly and daily averages. It showed that the variation in the levels of pollutants during the hours or the days was related to human activities. Where pollutants begin to increase with the onset of human activity and are at their lowest levels during the hours of the night or with the rest of the residents of GC. PM10 and NO\u003csub\u003e2\u003c/sub\u003e have two peaks during the day, which meet the two peaks of traffic density [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. It is also clear that the lowest level of all pollutants was Friday, which is the weekly holiday in Egypt. The most important observation was the refraction of pollutant levels with the increase in the maximum temperatures during the day, which indicates the effect of increasing the mixing layer height below the planetary boundary layer as well as the increase in wind speeds on the dispersion of pollutants in GC. The variation in the level of the combined arithmetic averages for the months during the year confirmed that the level of pollutants is at its lowest levels during the summer months, which indicates the effect of increasing the planetary boundary layer height and increasing the average temperatures, which has an impact on the improvement of air quality in GC.\u003c/p\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eExploratory data analysis (EDA) of pollutants in GC clearly showed that the issue of air pollution is due to the problem of increasing particulate matter concentrations, whether in short-term exposure or long-term exposure. As for gaseous pollutants, they did not exceed the guidelines of WHO or the regulations of the Egyptian Environmental Law for short-term exposure, and some violations were recorded in areas with high traffic or heavy industrial activity for long-term exposure.\u003c/p\u003e \u003cp\u003eIt turns out that sandstorms in GC are associated with gusty southern winds higher than 5 m/s. And its frequency is significantly higher with advancement in the hours of the day than during the night. On the contrary, the haze occurs during calm winds of less than 3 m/s, and their highest frequency is during the early morning hours, and gradually fades with progress in the daylight hours. In general, weather factors help disperse local pollutants during daylight hours due to moderate winds and increasing mixing layer height. While the weather factors help the concentration of pollutants during the early morning and evening hours, due to the calm winds and the decreasing mixing layer height.\u003c/p\u003e \u003cp\u003eAs for the annual pattern, the weather factors help to disperse local pollutants during the summer months due to wind speeds are higher than in the winter months, and the mean mixing layer height increases due to the mean temperature increases. The effect of the terrain is also evident in the dispersal of local pollutants, as there was a positive correlation between the elevation of the monitoring sites and pollutant averages. This caused stations located in the Nile River basin to have higher concentrations.\u003c/p\u003e \u003cp\u003eThis suggests that the sources of pollution in GC are either natural sources as a result of raising the accumulated dust with the wind blowing, or as a result of traffic congestion and large, planned, or small and craft industrial activities. Recent results indicate the successful control of the open burning of agricultural residues and biomass due to the efforts of the Egyptian Ministry of Environment in confronting acute air pollution episodes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e The authors declare that this work has not received any financial support (No funds, grants, or other support was received).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest/Competing interests\u003c/strong\u003e: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data\u003c/strong\u003e: The raw data analyzed during the present study are available from the Egyptian Environmental Affairs Agency on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e (software application or custom code): Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMohammed Mahmoud Hwehy: Conceptualization Ideas, Methodology Development, Validation, Formal analysis, Resources Provision of study, Visualization Preparation, and Writing - Original Draft Preparation\u003c/p\u003e\n\u003cp\u003eFawzia I. Moursy: Supervision, Methodology Development, and Writing - Review\u003c/p\u003e\n\u003cp\u003eAttia M. El-Tantawi: Supervision, Methodology Development, and Writing - Review\u003c/p\u003e\n\u003cp\u003eMostafa A. Mohamed: Conceptualization Ideas, Data Curation, Validation, and Writing - Review\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e: Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e:\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e: Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGiannini A, Biasutti M, Held IM, Sobel AH. A global perspective on African climate. Clim Change 2008;90:359\u0026ndash;83. https://doi.org/10.1007/s10584-008-9396-y.\u003c/li\u003e\n\u003cli\u003eHarrop DO. Air Quality Assessment and Management. 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Egyptian Law for the Protection of the Environment number 4/1994 Amended by Law number 9/2009 and its Executive Regulation 2009. http://www.eeaa.gov.eg/en-us/laws/envlaw.aspx (accessed March 19, 2021).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Air Pollution, Terrain, Particulate Matter, Nitrogen Dioxide, Sulfur Dioxide","lastPublishedDoi":"10.21203/rs.3.rs-3185000/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3185000/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe accelerated urbanization in developing counties in the Middle East and North Africa (MENA) region increases exposure to outdoor air pollution. This work aims to evaluate the ambient air quality in the Greater Cairo area (GC) as one of the largest megacities in the MENA region. The World Health Organization (WHO) classified GC as the largest polluted city in the MENA region. Exploratory data analysis (EDA) was used to assess the pollutants data and meteorological data to show the impacts of weather factors on ambient air quality in the study area. The results show that GC suffers from particle matter (PM) pollutants for both long-term and short-term exposure. The short-term exposure to gaseous pollutants did not exceed the guidelines, however, the long-term did in some traffic areas. The weather and terrain show significant impacts on the temporal and spatial variation of pollutants observations. Most ambient air pollution issues in the MENA region are due to its natural sources and traffic.\u003c/p\u003e","manuscriptTitle":"Evaluation of the Air Quality in Arid Climate Megacities. (Case Study: Greater Cairo)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-08-08 15:09:57","doi":"10.21203/rs.3.rs-3185000/v1","editorialEvents":[{"type":"communityComments","content":1}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"522bf761-7563-4776-ac48-b1ed5f6ad67d","owner":[],"postedDate":"August 8th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-04-11T23:27:58+00:00","versionOfRecord":{"articleIdentity":"rs-3185000","link":"https://doi.org/10.31577/congeo.2024.54.1.6","journal":{"identity":"contributions-to-geophysics-and-geodesy","isVorOnly":true,"title":"Contributions to Geophysics and Geodesy"},"publishedOn":"2024-04-01 00:00:00","publishedOnDateReadable":"April 1st, 2024"},"versionCreatedAt":"2023-08-08 15:09:57","video":"","vorDoi":"10.31577/congeo.2024.54.1.6","vorDoiUrl":"https://doi.org/10.31577/congeo.2024.54.1.6","workflowStages":[]},"version":"v1","identity":"rs-3185000","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3185000","identity":"rs-3185000","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}