Abstract
First-ever measurements of particulate matter (PM2.5, PM10, and TSP) along with gaseous pollutants (CO, NO2, and SO2) were performed from June 2019 to April 2020 in Faisalabad, Metropolitan, Pakistan, to assess their seasonal variations; Summer 2019, Autumn 2019, Winter 2019–2020, and Spring 2020. Pollutant measurements were carried out at 30 locations with a 3-km grid distance from the Sitara Chemical Industry in District Faisalabad to Bhianwala, Sargodha Road, Tehsil Lalian, District Chiniot. ArcGIS 10.8 was used to interpolate pollutant concentrations using the inverse distance weightage method. PM2.5, PM10, and TSP concentrations were highest in summer, and lowest in autumn or winter. CO, NO2, and SO2 concentrations were highest in summer or spring and lowest in winter. Seasonal average NO2 and SO2 concentrations exceeded WHO annual air quality guide values. For all 4 seasons, some sites had better air quality than others. Even in these cleaner sites air quality index (AQI) was unhealthy for sensitive groups and the less good sites showed Very critical AQI (> 500). Dust-bound carbon and sulfur contents were higher in spring (64 mg g−1) and summer (1.17 mg g−1) and lower in autumn (55 mg g−1) and winter (1.08 mg g−1). Venous blood analysis of 20 individuals showed cadmium and lead concentrations higher than WHO permissible limits. Those individuals exposed to direct roadside pollution for longer periods because of their occupation tended to show higher Pb and Cd blood concentrations. It is concluded that air quality along the roadside is extremely poor and potentially damaging to the health of exposed workers.
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Introduction
Air pollutants are substances that are present in the air at concentrations that can harm humans, plants, and the environment. Some pollutants have both natural and anthropogenic sources. Anthropogenic emissions arise from transportation, industrial activities, agriculture, and energy production (Capes et al., 2009). The most common air pollutants are particulate matter (PM), nitrogen and sulfur oxides (NOx, SOx), carbon mono- and dioxide (CO, CO2), ozone (O3), and volatile organic compounds (VOCs). According to the World Health Organization (WHO, 2021), 9 out of 10 people breathe air that contains high concentrations of pollutants, and exposure results in an estimated 7 million premature deaths every year. The Global Burden of Disease Study found that outdoor air pollution is responsible for 4.2 million deaths every year, with fine particulate matter (PM2.5) contributing the most (Cohen et al., 2017). PM are categorized based on particle diameter in micrometers (µm); PM2.5, PM4, and PM10, collectively referred to as total suspended particulates (TSP). Carbon monoxide (CO) is a silent killer if inhaled and the United States Environmental Protection Agency (US-EPA) sets an 8-h average value at 9 ppm for this pollutant (US-EPA, 2015). In the absence of enough oxygen, incomplete combustion of fuel in vehicles, boilers, or incinerator fuels yields CO (Rossner et al., 2017), causing higher CO concentrations in their immediate vicinities. CO interrupts oxygen delivery to the organs and tissues of the human body. During the COVID-19 pandemic, air pollutant concentration was reduced significantly due to reduced transportation and industrial activities, indicating that there is a direct relationship between pollutant emissions and fossil fuels combustion (Sari & Kuncoro, 2021). NOx and SO2 are also produced by the combustion of fossil fuels and are thus emitted by vehicle exhausts, and from industrial chimneys (Javed et al., 2014; Kumar et al., 2018; Richter et al., 2005). Emission standards are available at US-EPA website for all pollutants (US-EPA, 2015). High concentrations of NOx and SO2 pollutants impact human health (Nukshab et al., 2020) causing lung irritation, acute respiratory illness, cardiovascular diseases, respiratory illness, bronchitis, emphysema, and reduction of gestational age (Anderson et al., 2018; Behrentz et al., 2004; Cakmak et al., 2014). Their emissions become higher in congested areas where more vehicles pass per unit of time (Liu et al., 2020) and sulfur is dangerous for the skin and also causes irritation in the eyes (Bandowe et al., 2019). Almost 50% of secondary aerosol mass is made up of carbon and contains cadmium (Cd) and lead (Pb) which are dangerous to exposed organisms, i.e., humans (Liu et al., 2023) and in Faisalabad (Shahid et al., 2012; Niaz et al., 2012; Javed et al., 2015, 2016; Bashir et al., 2023). These studies indicate that in Faisalabad brick kilns (Hamid et al., 2023), industry and vehicular emissions, including tire-derived particles (Adachi & Tainosho, 2004), are important sources. The recent work of Bashir et al. (2023) shows that for ten sites in Faisalabad Cd and Pb are the most common heavy metals present in particulate matter, followed by copper, nickel, and then zinc. The daily carbon and sulfur contents of air have been studied for the first time in Pakistan in this work. The aim of this study was to measure pollutant concentrations along the roadsides of the Faisalabad metropolitan area, to calculate seasonal variations, and to determine the potential impact of roadside pollution exposure on human health. We also calculated a novel AQI based on the measurement of six air pollutants defined by the US-EPA (U.S. EPA, 2023), World Health Organization (WHO, 2021), and Pakistan Environmental Protection Agency (Pak-EPA, 2010).
Materials and methods
Description of sampling sites
Faisalabad is the third largest city in Pakistan and is a major industrial hub. Thus, the city is popularly known as the Manchester of Pakistan. It is sandwiched between longitudes 73° to 74° east and latitudes 30° to 31.15° north. It has an area of 1230 km2. Faisalabad lies 184 m above sea level (Pasha et al., 2015). Figure 1 shows the major districts of the Punjab Province. Our study area lies between district Faisalabad (indicated as green) and district Chiniot (indicated as blue). Based on the preliminary survey, the selected sites have diverse area usage and are representative of urban, dense traffic areas, motorways, industrial areas, railway crossings, bridges, the river, and rural and agricultural areas. The study zone starts from the Sitara Chemical Industries, Sheikhupura Road, Faisalabad (FSD) to Masood Textile Mill, Sargodha Road, FSD via Khurrianwala, Gatwala Forest Park and Nishat Abad Towns and from Masood Textile Mill to Bhianwala, Sargodha Road, Tehsil Lalian to District Chiniot, via Chiniot and Chenab Nagar. The monitoring points (L1, L2, and so on) along the selected roads are shown in Fig. 1.
GIS-based map of the study area and sampling points along the selected road
Measurement of particulate and gaseous pollution
Detailed description of sampling locations is given in Table 1. Particulate and gaseous pollutants were measured at each location and a 3-km grid distance was maintained between each location. At each location and for each pollutant four concentrations were recorded each hour and these data were used to calculate average hourly concentrations. Micro-Dust Pro Real Time Aerosol Monitors (model HB3275-07, Casella CEL, UK) were used for PM2.5, PM10, and TSP measurements at each site, with instruments run for 6-h, periods between filter analysis. The instrument could detect 0.001 to 2500 mg m−3 at 0.001 mg m−3 resolution by absorption of infrared light using angles 12 to 20°. For size of particulate pollution, polyurethane foam filter (PUFF) having a flow rate of 3.5 L/min was used (Javed et al., 2014, 2015). Gaseous pollutants (CO, NO2, and SO2) were measured using the MIRAN SapphIRe Portable Infrared gas analyzer Model 205B. This instrument detects any compound with absorbance in the wavelength region from 7.7. to 14.1 µm or any of the available fixed band pass filters, such as CO, NO2, and SO2, hydrocarbons. To operate analyzers and ensure quality control, manufacturer’s Basic User Guidelines or Manuals were followed (https://www.equipcoservices.com/pdf/manuals/thermo_miran_sapphire.pdf).
Air quality index (AQI)
AQI is calculated and presented worldwide in order to provide single numbers which describe air quality based on measurements of a number of pollutants. There are a number of ways to calculate AQI (Kumari & Jain, 2017), and we adopted a relatively simple approach based on the PM2.5, PM10, TSP, CO, NO2, and SO2 monitoring data available to us. We used the formula described below in accordance with the established standards.
where C and S showed the concentration of respective pollutant and their standards set by the Pakistan Environmental Protection Agency (Pak-EPA, 2010 and Mir et al., 2024). Further AQI is categorized into 8 categories which are as follows: (0–50) good, (51–100) moderate, (101–150) unhealthy for sensitive groups, (151–200) poor and unhealthy, (201–300) very poor and unhealthy, (301–400) hazardous, (401–500) very hazardous and (> 500) very critical (Pak-EPA, 2010).
Dust-bound carbon and sulfur contents
Total suspended particulate (TSP) mass samples were collected at each site on micro glass fiber filter papers (47 mm) using a high-volume air sampler (model CF-1001BRL, Hi-Q, USA). These filters were used because they are inert, highly pure, capable of retaining particles down to 0.01 µm size, have high PM collection efficiency (97%), and very low moisture absorption capacity (Method IO-3.1, 1999). Moreover, the protective covering of a Hi-Vol sampler protects the instrument as well as the high-speed motor which acts as a blower. The samples were taken 10 m away from the road at a height of 5 m above ground level. TSP mass was determined gravimetrically from the filters after conditioning in desiccator for 24 h at 45 ± 5% relative humidity and 23 ± 3 °C temperature. After the entire process, each filter paper containing PM was stored in an aluminum foil and sent to the lab for carbon and sulfur analysis (US EPA, 2015). One gram dust was used to analyze carbon and sulfur contents using Infrared Carbon Sulfur Analyzer CS996 at the Punjab Bioenergy Institute, Faisalabad (SPM, 1999).
Human blood sampling
Human blood samples from 20 individuals (numbered P1 to P20) from the selected sampling sites were obtained once in each of the four seasons. Contact phone numbers were obtained to ensure that the same individuals could be contacted and resampled. Fresh venous blood samples were collected using sterile syringes Luer-lok tip with BD Precision Glide Needle 23G × 1W (0.6 mm × 25 mm) and stored in 5 ml K2 EDTA (K2E) 5.4 mg blood collection tubes at 4 °C till further analysis. A questionnaire-based survey was conducted to gain information of the sampled individuals to obtain their demographic status, i.e., food intake, drinking water source, and major air pollutant exposure (proximity to known sources). Medical information of the targeted population was also collected.
Digestion of blood samples for analysis of Cd and Pb on flame atomic absorption spectrophotometer
The blood samples were digested and analyzed using conventional methods (see Mahmood et al., 2014). For this 0.5 mL of a whole blood sample was taken in a Pyrex flask. The digestion mixture (nitric acid and hydrogen peroxide) was prepared using the 2:1 ratio, by volume. Then 3 mL of digestion mixture was added to the flask containing the blood sample and allowed to stand for 10 min. Flasks were then heated on a hot plate at 60–70 °C for 1 to 2 h. After that 2 mL of nitric acid was added with a few drops of hydrogen peroxide at 80 °C until a clear digest is obtained. The excess acid was allowed to evaporate and then diluted with 0.1 N nitric acid. Then these samples were transferred to a 25-mL volumetric flask and double distilled water was added to make up the volume. The blank sample was also prepared (Popoola et al., 2019) using the same procedure without the blood sample. Cd and Pb were analyzed on atomic absorption spectrophotometer (AAS). The coordinates of each sampling location are already presented against the location number in Table 1. Details of each patient are given in Table 2 along with their occupation.
GIS map**
Inverse distance weighted (IDW) method was used using ARC GIS 10.8 for interpolation of the concentration of air pollutants at each site. In IDW, technique sampling points were joined together and weighted according to their distance from the point being interpolated, hence creating a surface grid (Ahmad et al., 2012; Briggs et al., 1997; Parveen, 2012).
Statistical analysis
Relationship between measured air pollutants were explored using correlation analysis. Descriptive statistics were applied to the data set and mean, standard deviation (St. Dev.), Coefficient of variation, skewness, kurtosis, minimum, and maximum were calculated for each pollutant. Excel 365, JASP 0.8.5.1, Origin Pro, and SPSS software were used for data processing and visualization. Skewness and kurtosis values indicated normal distributions. These values are shown in the Supplementary Information.
Results and discussions
Seasonal and spatial variation of PM2.5, PM10, TSP, and CO
Each particulate pollutant concentration was categorized into seven colors which represent the quality of air, i.e., green (good), yellow (marginal), orange (unhealthy for sensitive groups), red (poor or unhealthy), purple (very poor or very unhealthy), maroon (hazardous), dark maroon (very hazardous) and blue (very critical). The concentration ranges which equate to each color are shown in Fig. 2 (Gurjar et al., 2008; U.S. EPA, 2012). The lighter colors represent the lowest concentration and darker colors represent the highest concentrations (Ahmad et al., 2012). These categories were used to create a seasonal and spatial distribution of PM2.5, PM10, and TSP.
Interpolation of seasonal variations of a PM2.5, b PM10, c TSP, and d CO concentrations in Summer 2019, Autumn 2019, Winter 2020, and Spring 2020 (left to right)
The spatial interpolation maps during summer, autumn, winter, and spring, are shown in Fig. 2. This Figure clearly indicates the spatial variability of PM2.5 concentrations along the sampling roads; these ranged across five concentration categories from poor–unhealthy (55.5–150.4 μg m−3) to very critical (> 500 μg m−3). During summer lower concentration was found at locations L1 to L4—shown as red color in Fig. 2. Higher concentrations were found at locations L12, L14, L16, L19, and L20—shown in blue color. The blue color showed that the concentration of PM2.5 was above 500 μg m−3 which is very critical. Seasonal distribution shows that concentrations of PM2.5 in each season were above 55.5 μg m−3 which is categorized as poor and unhealthy. TSP showed similar spatial patterns as for PM2.5 (lower values at locations 1 to 4 and higher values at the other locations). As would be expected TSP concentrations were higher than those for PM2.5 and PM10 and their magnitudes were very critical at most locations in all four seasons (Fig. 2). The PM10 concentrations showed slightly different spatial patterns with some of the locations in the middle of the transect showing very critical (blue) values in summer, spring and autumn. The CO concentrations showed a similar spatial pattern to that seen for PM10 (Fig. 2). These maps clearly visualize the area of low, moderate, and higher concentration of PM2.5, PM10, TSP, and CO during each season. Poor road structures, poor maintenance of vehicles, and congested road with high traffic density are the reasons of higher concentrations of pollutants along the selected road (Ashraf et al., 2010; Li et al., 2023; Piracha & Chaudhary, 2022; Xu et al., 2022). These results, with very few periods of air quality at moderate or good levels, suggest that better management of air quality in the mapped areas is important for stakeholders.
For NO2 our observed seasonal average concentrations were Summer 2019–71.98 µg m−3, Autumn 2019–53.02 µg m−3, Winter 2020–57.04 µg m−3, and Spring 2020–35.02 µg m−3 (see Supplementary Table 1). Thus, all seasonal average NO2 concentrations exceed the 2021 WHO Air Quality Guideline level which is an annual average of 10 µg m−3. With values at 64.7, 42.07, 50.82 and 39.28 µg m−3 for Summer 2019, Autumn 2019, Winter 2020, and Spring 2020, respectively (see Supplementary Table 1), our measured seasonal SO2 concentrations also exceeded, or in the case of spring, were very close to the WHO AQG level of 40 µg m−3.
Seasonal pattern of AQI based on six studied pollutants at each location
Figure 3 shows the seasonal variation of the air quality index (AQI) at each location based on PM and gaseous pollutant concentrations. The red color indicates a lower AQI (116), while blue indicates a higher AQI (732) during different seasons at each location. The overall air quality was categorized as very poor and unhealthy for location 2 during each season to very critical for L16, L14, etc. as shown in Fig. 3. AQI of summer and spring were clustered in the same group associated with autumn and winter AQI levels. The primary clusters were found between L1 and L2, L4 and L8, L5 and L9, L27 and L30, L22 and L28, L10 and L25, L12 and L13, L15 and L24, L14 and L16 which shows that these locations have the same anthropogenic source of emission, i.e., vehicles.
Heat map with dendrogram of seasonal variation in AQI based on PM2.5, PM10, TSP, CO, NO2, and SO2
Summary of means and standard errors of particulate and gaseous pollutants
The three pollutants (PM2.5, PM10, TSP) all showed the highest concentrations in summer, and were lowest in autumn or winter (see Fig. 4). The seasonal fall in PM concentrations (PM2.5, PM10, and TSP) and gaseous pollutant (CO, NO2, and SO2) from summer to autumn could reflect “washout” associated with monsoon rainfall which occurs from July through to September and the winter rise in gaseous pollutants probably arise from increased fuel use. But we feel that the high PM concentrations recorded here throughout the year reflect that the roads are unmade-up, in poor condition and have bare ground along the roadside. The high PM and gaseous pollutant values which we recorded result from the high and whole-year emissions from Faisalabad’s roads, local chemical and textile industries and the wood-based power plant. CO, SO2, and NO2 also all had the highest concentrations in summer or spring. NO2, SO2 and CO had the lowest concentrations in winter (see Fig. 4). Standard deviation (St. Dev.), coefficient of variance (CV), skewness, kurtosis, minimum, and maximum of particulate and gaseous pollutants are given in (Supplementary Table 1).
Seasonal variation of all pollutants with means (X) and median, interquartile range, maximum and minimum values in box notation
Seasonal variation of carbon and sulfur contents bound with road dust
Dust-bound carbon content (CC) was highest in the summer and spring (Fig. 5a). The lowest values of CC were at locations L8 and L9 (14.67 ± 0.94 and 23.00 ± 1.63 mg g − 1 respectively (means and SE) which are forest and a commercial urban area where the road was in good condition (see Table 1). The highest CC was 87.33 ± 2.49 and 91.67 ± 1.25 mg g−1 at locations L16 and L17, respectively. Both these locations have vehicular emissions in an urban setting where agricultural practices are also involved. Here the road structure is also poor where vehicles use more fuel and produce more exhaust along with unburnt particles. When vehicles pass the road, dust is re-suspended and increases the pollution load in the surroundings. In a Chinese study, CC was analyzed in street dust, and it was found that CC ranges from 5 to 71 mg g−1 (Bandowe et al., 2019). CC was mainly generated from the exhaust of vehicles and coal and wood burning in industries and crop burning (Lin et al., 2020). 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Hamaad Raza Ahmad and Muhammad Shahbaz did the technical checks during the analysis while Abdul Nasir provided the facilities to measure the gas concentrations, and Peter Freer Smith reviewed the manuscript comprehensively. All authors read the manuscript and approved it for publication. The work described was carried out in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans. The manuscript should be in line with the Recommendations for the Conduct, Reporting, Editing and Publication of Scholarly Work in Medical Journals. All authors have read, understood, and have complied as applicable. The authors declare no competing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Below is the link to the electronic supplementary material. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Zeeshan, N., Murtaza, G., Ahmad, H.R. et al. Particulate and gaseous air pollutants exceed WHO guideline values and have the potential to damage human health in Faisalabad, Metropolitan, Pakistan.
Environ Monit Assess 196, 659 (2024). https://doi.org/10.1007/s10661-024-12763-3 Received: Accepted: Published: DOI: https://doi.org/10.1007/s10661-024-12763-3Data availability
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