Characterization of five-year observation data of fine particulate matter in the metropolitan area of Lahore
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This study aims to assess the long-term trend of fine particles (PM2.5; ≤2.5 μm) at two urban sites of Lahore during 2007–2011. These sites represent two distinct areas: commercial (Townhall) and residential cum industrial (Township). The highest daily mean concentrations of PM2.5 were noted as 389 and 354 μg m−3 at the Townhall and Township sites, respectively. As expected, the annual seasonal mean of PM2.5 was about 53 and 101% higher during winter compared with the summer and monsoon/post-monsoon seasons, respectively. On contrary to many observations seen in developing cities, the annual mean PM2.5 during the weekends was higher than weekdays at both monitoring sites. For example, these were 100 (142) and 142 μg m−3 (148) during the weekdays (weekends) at the Townhall and Township sites, respectively. The regression analysis showed a significant positive correlation of PM2.5 with SO2, NO2 and CO as opposed to a negative correlation with O3. The bivariate polar plots suggested a much higher influence of localized sources (e.g., road vehicles) at the Townhall site as opposed to industrial sources affecting the concentrations at the Township site. The imageries from the MODIS Aqua/Terra indicated long-range transport of PM2.5 from India to Pakistan during February to October whereas from Pakistan to India during November to January. This study provides important results in the form of multiscale relationship of PM2.5 with its sources and precursors, which are important to assess the effectiveness of pollution control mitigation strategies in Lahore and similar cities elsewhere.
KeywordsFine particles Air quality monitoring Meteorological parameters Criteria pollutants Health risk
Lahore is a metropolitan area with high levels of particulate pollution that often surpasses the guideline values of World Health Organization (WHO) and the National Ambient Air Quality Standards (NAAQS) of Pakistan (Pak-EPA 2005). Both fine and coarse particulate matter cause various types of health concerns (e.g., Stone et al. 2010; Kim et al. 2011; Tsiouri et al. 2015; Lan et al. 2016). The WHO estimated ∼360,000 premature deaths in Asia each year due to air pollution (WHO 2008). The environmental degradation, including water and soil, is about 6% of Pakistan’s GDP, and the indoor and outdoor air pollution contributes nearly half of it towards the total illness and premature mortality (World Bank 2006). The lack of stringent implementation of air pollution regulations and the mass transportation system contribute further to the issue of local air pollution (Biswas et al. 2008). Needless to mention that the particulate matter (PM) also plays an important role in affecting the global climate (IPCC 2007; Karagulian et al. 2015).
The increasing population and urbanization have led to an increase in numerous industrial sources as well as the road vehicles (Biswas et al. 2008; Stone et al. 2010; Shah et al. 2012; Rasheed et al. 2015; Ali et al. 2015; Molina et al. 2017). New evidence related to exposure risk assessment and global exposure estimates shows that the exposure to the ambient PM has increased than previously estimated (WHO 2014a). In megacities such as Lahore, important factors for the increased exposure to air pollution are the higher intensity of human activities and emissions from the road vehicles. PM is currently considered to be one of the best indicators for assessing health impacts caused by the ambient air pollution (WHO 2014a; Yao et al. 2015).
Summary of the past selected PM studies carried out in Pakistan
Concentration (μg m−3)
Lahore (roadside monitoring)
5–10 April 2001
Lahore (roadside monitoring)
Ghauri et al. (2007)
Lahore (Pakistan Upper Atmospheric Research Commission Office)
December 2005 to February 2006
Biswas et al. (2008)
Lahore (University of Engineering and Technology, Lahore, UET)
PM10, OC, EC
February to March 2006
Zhang et al. (2008)
Lahore (Campus Bridge, Punjab University and Thokar Niaz Baig Chowk)
Ali et al. (2015)
Schneidemesser et al. (2010)
PM2.5 Metrological Parameter
72.7 ± 55.2
Rasheed et al. (2015)
Stone et al. (2010)
Lahore (19 different residential and commercial sites)
June to August 2012
Ashraf et al. (2013)
Lahore (UET Kala Shah Kaku site, UET Campus site and Lahore University of Management and Sciences)
Aerosol optical depth (AOD)
Khokhar et al. (2016)
The distribution and transport of PM in the atmospheric environments are markedly associated with meteorological parameters such as the wind speed, wind direction, relative humidity (RH), rainfall and ambient temperature (Pakbin et al. 2010). Therefore, PM concentrations and meteorological data should be evaluated statistically in order to develop correlations that can assist in identifying sources and thereby in the design of cost-effective emission control strategies (Ragosta et al. 2008). The data of ambient air quality are crucial in air resource management but are largely unavailable for rapidly growing cities of Pakistan. The analysis of a 5-year long-term data set provides significant insight into the factors that drive seasonal variations in PM, their relationship with meteorological parameters and criteria pollutants. This work could be used as an incentive to initiate other studies on trend analysis. It is also anticipated that the findings of this study would be of high relevance for designing and instituting future abatement strategies and emission regulations for the pollution control in rapidly developing cities such as Lahore.
The objective of this paper is to assess the long-term trend of fine particles PM2.5 at two different urban sites of Lahore (Pakistan) between 2007 and 2011. The trend of PM2.5 is compared with Pakistan National NAAQS and WHO guidelines. The seasonal changes in PM2.5 and their underlining reasons during weekdays and weekends, together with the correlation of PM2.5 with other pollutants and meteorological parameters, were also assessed. The AERONET data, backward trajectory and MODIS imageries were used to analyse the long-range transportation of PM and its seasonal contribution. The overall aim of these analyses is to form a basis for the development of appropriate regulatory strategies for limiting the exposure to ambient PM.
Summary of instrument used for the measurements
Name of the instrument
Fraction of data available
Horiba Ltd. Model APNA-370
Nondispersive infrared ray method (ISO4224)
NOx, NO, NO2
Horiba Ltd. Model APNA-370
Horiba Ltd. Model APSA-370
UV fluorescence method (ISO10498)
Horiba Ltd. Model APOA-370
UV photometry method
Horiba Ltd. Model APDA-370
β-Ray absorption method (ISO6349)
Observation data and analysis
A data management and reporting software (IDA-ZRW) by HORIBA was used to collect and manage the data at both the ambient air quality monitoring stations. The statistical techniques such as Stata 3, R (Studio) and remote sensing tools such as AERONET were used further for the development of correlation of PM2.5 with meteorological and pollutant parameters. PM2.5 during weekdays and weekends and across 5 years was calculated, along with the exceedance factor, box plots, wind rose and bivariate polar plots. The satellite imageries from MODIS, backward trajectory and almucantar inversion were used to extract further data on the PM2.5 among different seasons, their sources and dispersion conditions. The almucantar inversion finds the minimum size intervals of PM from 0.439 to 0.992 μm (Dubuisson et al. 1996). This minimum size interval is used as a separation point among fine and coarse particles. It also estimates the effective radius, volume median radius, standard deviation and volume concentrations for both fine and coarse particles.
Results and discussion
Temporal trend of PM2.5
The daily mean concentration of PM2.5 during weekends (Saturday–Sunday) was relatively higher than the weekdays (Monday–Friday) at both monitoring sites of Lahore. This is an interesting finding, which is opposite to many cities worldwide where much lower concentrations are usually reported during the weekends (Al-Dabbous and Kumar 2014; Yadav et al. 2014). For examples, the mean PM2.5 during the weekdays at the Townhall sites was measured as 95 μg m−3 as opposed to 100 μg m−3 during the weekends; the corresponding values were 142 and 148 μg m−3 at the Township site, respectively (Fig. 3b). The predominant reason for this interesting trend is that a relatively higher number of people living in surrounding suburban/rural areas visit Lahore for recreational purposes during the weekends, which is a typical feature of many Asian cities that result in increased traffic volume and in turn the PM2.5.
Primary emissions of PM10 and PM2.5 decreased by 14 and 16%, respectively, in the EU-27 in 2011 compared with 2002–2011 levels (Ikeda and Tanimoto 2015). The reductions in the same period for the 32 member countries of the European Union were 9% for PM10 and 16% for PM2.5, respectively (Ikeda and Tanimoto 2015). In a WHO study, a total of 795 towns/cities from 67 countries were selected; 641 cities represent the high-income countries and 55 represent the middle- and low-income countries with available data of PM10/PM2.5 from 2008 to 2013. It was found that globally PM levels were increased by about 8%. The 90% of the low- and middle-income cities assessed exceeded annual WHO guidelines for PM10 and PM2.5. The worldwide future trends in PM10 and PM2.5 concentrations show a decrease in 30% of the regions as opposed to modest or increasing trend in the remaining 70% of the regions (WHO 2016). This study clear falls within the rest of 70% regions with increasing PM2.5 concentrations as is also the case with the most cities in developing countries (WHO 2016). The annual exceedances at the selected sites of Lahore were between 100 and 500% (Fig. 4e–h), indicating much higher concentrations compared with those reported in studies of European or high-income countries elsewhere (Ikeda and Tanimoto 2015; WHO 2016).
Bivariate polar plots
The colour scale of bivariate polar plots of PM2.5 shows the concentration, and the radial scale shows the wind speed. The concentration increases from the centre of the plot radially outwards in some cases while an opposite trend is seen in other cases. Bivariate polar plots of Townhall indicate that PM2.5 sources were mostly localized as depicted by high concentrations in the centre at low wind speeds, mainly contributed by the emissions from road vehicles (Fig. 5). A slight shift towards the southwest direction in monsoon/post-monsoon season at the Townhall was due to increased precipitation (Fig. 6). The annual bivariate polar plot of Townhall in 2011 showed a shift towards southwest due to intense construction activity of a 27-km-long bus rapid transit system in Lahore (Fig. 5); both the annual and seasonal bivariate polar plots for the Township indicate transport of PM2.5 to the site from the presence of industrial areas in the east and southeast direction of air monitoring station (Figs. 5 and 6).
Correlation of PM2.5 with the criteria pollutant and meteorological parameters
The correlations among the significant meteorological parameters such as wind speed, ambient temperature, RH and PM2.5 show a negative correlation with temperature (Fig. 7e) and wind speed (Fig. 7f) and no correlations with the RH (Fig. 7g). This demonstrates the fact why PM2.5 concentrations were much higher in winter than in summer (Fig. 3a) due to a decrease in temperature and wind speed. Such higher levels raise a number of concerns including reduced visibility affecting the speed of on-road vehicles and the increased cases of both chronic and acute respiratory and cardiovascular health problems in the region, as discussed by previous studies (Tiwari et al. 2013; Yin et al. 2016).
MODIS fires hotspots and the effect of transboundary pollution
Size distribution of aerosol particles
Summary and conclusions
We assessed the temporal trend of fine PM (PM2.5) over a period of 5 years in Lahore. The annual mean PM2.5 concentrations were found to be increasing at Township site and show no clear trend at the Townhall site during the study period. Our findings show that the levels of PM2.5 reach to their highest levels during the winter season. For example, the highest daily mean PM2.5 measure at Townhall and Township was found to be 389 and 354 μg m−3, respectively.
The annual average minimum PM2.5 was found to be 52 μg m−3 at Townhall during 2010 while the average maximum PM2.5 was 280 μg m−3 at Township during 2009. PM2.5 crossed 98% daily and 100% annual permissible limits of NAAQS and WHO guidelines at both sites of Lahore. The average concentrations during the winter were found to be about 53% higher than those during summer and almost double than the monsoon/post monsoon, mainly due to a decrease in temperature and stagnant climatic conditions. Seasonal air quality trend of Lahore from 2007 to 2011 was analysed and found that the highest annual mean PM2.5 in winter was 157–171 μg m−3, summer 99–115 μg m−3 and monsoon/post-monsoon 66–97 μg m−3 at Townhall and Township, respectively.
PM2.5 during weekdays was usually less by up to 4% than weekends. The annual EF of PM2.5 with respect to WHO guidelines lies within the range of 3–14 and 6–12 with respect to NAAQS of Pakistan at Townhall and Township sites, respectively. The daily and annual % increases lie in the range of 100–500% with respect to WHO guidelines at both monitoring sites of Lahore.
The sources contributing to PM2.5 at the Townhall site were mostly localized as opposed to Township where there is the influence of transported emissions from the adjacent industrial sites. Correlation of PM2.5 with CO, NO2 and SO2 was positive and negative with O3. However, the correlation of PM2.5 with meteorological parameters such as temperature and wind speed was negative and nonsignificant with RH. Retrieved MODIS Aqua/Terra imageries, together with predominant wind direction, showed the influence of transboundary air pollution from India towards Lahore during the months of March to October as opposed to an opposite trend during the months of November to February when the long-range transport of PM2.5 is from Lahore to India.
This study contributes to understanding the long-term trend of PM2.5 in the urban environment of Lahore. Our findings are important to understanding the surrounding sources and underline the factors that bring the seasonal variability in PM2.5. Further studies require the monitoring at a greater number of sites to broaden the understanding of spatial variability across the city along with a physicochemical analysis of the fine particles.
The authors are grateful to the Higher Education Commission (Pakistan) and the Environmental Protection Agency, Punjab (Lahore), for the funding support to Fatima Khanum that enabled us to carry out this research work. We also thank Mr. Farooq Alam (research officer, Air Pollution Lab at the EPA), Mr. Toshiharu Ochi (JICA expert) and Mr. Hassan Murtaza Khan (statistical analyst) for their valuable suggestions and contributions to this work.
- Ali Z, Rauf A, Sidra S, Nasir ZA, Colbeck I (2015) Air quality (particulate matter) at heavy traffic sites in Lahore, Pakistan. J Anim Plant Sci 25:644–648Google Scholar
- Ashraf N, Mushtaq M, Sultana B, Iqbal M, Ullah I, Shahid AS (2013) Preliminary monitoring of tropospheric air quality of Lahore City in Pakistan. Int J Chem Biochem Sci 3:19–28Google Scholar
- Bureau of Statistics (2015) Punjab development statistics 2015. Government of Punjab, LahoreGoogle Scholar
- IPCC (2007) Climate Change 2007. Impacts, adaptation and vulnerability: Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel. Genebra, Suíça (accessed 07.08.2016)Google Scholar
- Kumar S, Srinivas N, Sunil KA (2014) Monitoring and assessment of air quality with reference to dust particles (PM10 and PM2.5) in urban environment. Int J Res Eng Tech 3:2321–7308Google Scholar
- Pak-EPA (2005) State of the environment report. Pakistan Environmental Protection Agency, Ministry of Environment, Government of Pakistan. Available from: http://environment.gov.pk/state-of-environment-report/ 27–6-2016 (accessed 08.07.2016)
- Rasheed A, Aneja VP, Aiyyer A, Rafique U (2015) Measurement and analysis of fine particulate matter in urban areas of Pakistan. Aerosol Air Qual Res 15:426–439Google Scholar
- Singh RP, Kaskaoutis DG (2014) Crop residue burning a threat to south Asian air quality. Earth Space Sci 95:333–334Google Scholar
- WHO (2008) Health topics: air. World Health Organization, Regional Office for the Western Pacific. wpro.who.int/health topics/air 4–6-2016 (accessed 15.07.2016)
- WHO (2014) Ambient air quality and health. Fact sheet No 313. WHO media centre. Available at: http://www.who.int/mediacentre/factsheets/fs313/en 21-5-2016 (accessed 15.07.2016)
- WHO (2016) Urban ambient air pollution database, 0.2, Public Health, Social and Environmental Determinants of Health Department, World Health Organization, 1211 Geneva 27, Switzerland. Available at: http://www.who.int/phe/health_topics/outdoorair/databases/cities/en/1-9-2016 (accessed 12.07.2016)
- World Bank (2006) Pakistan strategic country environmental assessment, South Asia environment and social development unit south, The World Bank, 36946-PK. Available at:http://siteresources.worldbank.org/SOUTHASIAEXT/Resources/Publications/448813-1188777211460/pakceavolume1.pdf 25–5-2016 (accessed 05.06.2016)
- Yadav R, Sahu LK, Jaffrey SNA, Gufran B (2014) Temporal variation of particulate matter and potential sources at an urban site of Udaipur in western India. Aerosol Air Qual Res 14:1613–1629Google Scholar
- Yao L, Lu N, Yue X, Du J, Yang X (2015) Comparison of hourly PM2.5 observations between urban and suburban areas in Beijing, China. Int J Environ Res 12:12264–12276Google Scholar
- Yin D, Zhao S, Qu J (2016) Spatial and seasonal variations of gaseous and particulate matter pollutants in 31 provincial capital cities, China. Air Qual Atmos Health 10:1–12Google Scholar
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