Design of air quality monitoring network of Luanda, Angola: Urban air pollution assessment

doi:10.1016/j.apr.2021.101128

Atmospheric Pollution Research

Volume 12, Issue 8, August 2021, 101128

https://doi.org/10.1016/j.apr.2021.101128 Get rights and content

Highlights

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Air quality monitoring network with 15 stations was proposed for Luanda.
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First air quality campaigns were performed at two monitoring sites.
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Concerning air pollution levels were measured comparing to EU limits.
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The effect of weekend on air pollution was assessed.

Abstract

The World Health Organization has been making recommendations on assessing and monitoring air quality for human health protection. Its implementation implies a spatial distribution plan for air quality monitoring sites, especially in populated cities where high emissions of air pollutants with anthropogenic origin are observed. Therefore, the present study aimed: (i) to propose the spatial distribution of air quality monitoring sites in the city of Luanda (capital of Angola); and (ii) to determine the PM_2.5, PM₁₀, NO₂, SO₂ and CO concentrations at two monitoring sites (Avenida Deolinda Rodrigues, ADR, and Condomínio Vale do Talatona, CVT), during four weeks for each site. Due to the absence of national legislation, air quality measurements were then compared to the limits defined by the European Union through Directive, 2008/50/EC. At ADR site, the average concentrations for PM₁₀, PM_2.5 and SO₂ exceeded the limits recommended for human health. At CVT site, CO, NO₂, PM₁₀, PM_2.5 and SO₂ concentrations were lower than the values measured at ADR site, due to the relative location of important fixed air pollutant emission sources: airport, thermoelectric plants, refinery, cement plants and the Port of Luanda. The weekend effect was also assessed at both sites. At ADR site, NO₂, SO₂, PM_2.5 and PM₁₀ concentrations were higher at weekends than on weekdays. At CVT site, NO₂, SO₂ and PM_2.5 concentrations were higher on weekdays compared to weekends. The present research will contribute to the decision-making process by the environmental health regulator in Luanda.

Introduction

Free access to clean air is one of the fundamental human rights (WHO, 2000). However, several pollutants are emitted into the atmosphere from natural or anthropogenic sources, contributing to the degradation of air quality, which puts at risk both the health of the population, vegetation and the built heritage (Cardoso, 2002). According to the WHO (2016) report, over 92% of the world's population live in regions where air pollutant concentrations are above the limits considered safe. Mabahwi et al. (2015) concluded that heart and lung pathologies are significantly more common in people who breathe polluted air than in groups living in cleaner atmospheres. Anthropogenic sources of pollution caused by industrial and urban development lead to accelerated air quality degradation and reduce the quality of life of their inhabitants (Boubel, 1994).

In developed and industrialized countries such as the United States, Canada and China, air pollution in urban areas is extensively investigated and characterized (Chen et al., 2013; Matte et al., 2013; Oiamo et al., 2012). Notwithstanding in developing countries, there is a scarcity of air quality monitors, and the eventual investigated data are not efficiently disseminated (Carvalho, 2016). Whittaker et al. (2020) conducted a research study to determine the concentrations of air pollutants (NO₂, O₃, SO₂, PM₁₀ and PM_2.5) in St. Kitts and Nevis, in the Eastern Caribbean. They found that the concentration of pollutants was high in urban areas, being positively correlated, except for PM₁₀. This air quality monitoring provided unprecedented information on a range of pollutants and their determinants, having contributed to an in-depth knowledge of air pollution in small developing countries.

Strategies to mitigate the harmful effects of high pollutant concentrations must be developed and implemented efficiently and cost-effectively to prevent public health risks (WHO, 2000). According to Gollata and Newig (2017), one of the most difficult challenges is to select appropriate locations for the implementation of air quality monitoring sites when designing a monitoring network. Optimising the number of sites, which configure the monitoring network, reduces costs without changing the air quality characterisation of the region. Therefore, Castro and Pires (2019) defined three fundamental criteria to be taken into account when choosing the site to optimise a network of air quality monitoring sites: (i) the representativity of the site, preferably those with high concentrations of pollutants; (ii) the number of monitored pollutants per site should be maximised; and (iii) the distance between monitoring sites. Macpherson et al. (2017) demonstrated that one way to optimise a monitoring network would be to identify redundant sites that should be excluded because they encumber the monitoring process and introduce interpretation biases on air quality. In the Republic of China, Wang et al. (2018) developed an innovative system consisting of a combination of correlation analysis, principal component analysis and cluster analysis, which ultimately helped to identify redundant monitoring sites. Three redundant sites were thus identified, resulting not only in cost savings but also in the optimisation of the city's air monitoring networks in reference.

The Institute for Security Studies of South Africa has estimated that 26 African countries will double the population growth between 2017 and 2050. Half of Africa's population will live in urban areas by 2035 (Bello-Schünemann and Aucoin, 2016). Specifically, the city of Luanda (capital of Angola) has been experiencing a population explosion in urban areas since the mid-20th century: from a population of 224 540 in 1960 to 8 234 098 in 2014 (Amaral, 1969; INE, 2016). In this context, the present work proposed a spatial distribution plan of air quality monitoring sites in Luanda according to main air pollutant emission sources in the city. Also, this study comprises: (i) the first reported monitoring campaigns of sulphur dioxide (SO₂), nitrogen dioxide (NO₂), particulate matter (PM₁₀, PM_2.5) and carbon monoxide (CO) concentrations at two urban sites in Luanda over four weeks; (ii) the assessment of the weekend effect on the concentration of the different pollutants; and (iii) the determination of the correlation between the different pollutants in each air quality monitoring site.

Section snippets

Studied area

Luanda is limited to the west by the Atlantic Ocean, to the north by Bengo province, to the east by Cuanza Norte province and the south by Cuanza Sul province. It has a total area of 18 826 km², 25% of which are urban areas and 75% are rural areas. The largest part of the city is located 128 m above sea level (INE, 2016). Luanda has a population of approximately 8.2 million (INE, 2016).

The warm season lasts for 9 months (from 15th August to 15th May), and the average daily maximum temperature

Air quality characterisation

At ADR site, the hourly average NO₂ concentration ranged from 46.67 to 130.21 μg m⁻³. At CVT site, NO₂ concentrations were between 35.67 and 106.96 μg m⁻³. According to EU human health legislation, the hourly average concentration for NO₂ should not exceed 200 μg m⁻³ more than 18 times per year (Directive, 2008). Therefore, this limit was not exceeded at any monitoring site. Fig. 2 shows the average daily profile of NO₂ concentrations at ADR and CVT sites during four weeks of study. The values

Conclusions

This work has defined the location of important air quality monitoring sites in urban areas of Luanda, an overpopulated city with characteristics of cities in developing countries. Furthermore, NO₂, SO₂, CO, PM_2.5 and PM₁₀ concentrations were evaluated at two monitored sites. At ADR site, average concentrations of SO₂, PM_2.5 and PM₁₀ exceeded the limits set by the European Union for the protection of human health, while NO₂ and CO concentrations were within the recommended limits. The average

Author contributions

Conceptualisation, A.A.B. and J.C.M.P; Methodology, P.M.C., A.A.B. and J.C.M.P.; Software, P.M.C. and J.C.M.P.; Validation, P.M.C., A.A.B. and J.C.M.P.; Formal analysis, P.M.C., A.F.E., A.A.B. and J.C.M.P.; Investigation, P.M.C. and J.C.M.P.; Resources, A.A.B.; Data curation, P.M.C., A.F.E., and J.C.M.P.; Writing – original draft Preparation, P.M.C.; Writing – review & editing, P.M.C., A.F.E., A.A.B. and J.C.M.P.; Visualization, P.M.C., A.F.E., A.A.B. and J.C.M.P.; Supervision, A.A.B. and

Declaration of competing interest

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.

Acknowledgements

This work was financially supported by the Base Funding - UIDB/00511/2020 of the Laboratory for Process Engineering, Environment, Biotechnology and Energy – LEPABE - funded by national funds through the FCT/MCTES (PIDDAC); A.F. Esteves acknowledges the FCT Scholarship 2020.05477. BD; J.C.M. Pires acknowledges the FCT Investigator 2015 Programme (IF/01341/2015).

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Design of air quality monitoring network of Luanda, Angola: Urban air pollution assessment

Highlights

Abstract

Introduction

Section snippets

Studied area

Air quality characterisation

Conclusions

Author contributions

Declaration of competing interest

Acknowledgements

Atmos. Environ.

Sci. Total Environ.

Atmos. Res.

The Lancet Resp. Med.

Atmos. Pollut. Res.

Waste Manag.

J. Environ. Sci.

Atmos. Environ.

Atmos. Environ.

Atmos. Environ.

Proc.-Social Behav. Sci.

Environ. Model. Software

Energy Rep.

Environ. Pollut.

Atmos. Environ.

Atmos. Environ.

The Unheralded Polluter: Cement Industry Comes Clean on its Impact

Nota sobre a evolução da população de Luanda e dos seus muceques

A pilot study of gaseous pollutants' measurement (NO2, SO2, NH3, HNO3 and O3) in Abidjan, Côte d'Ivoire: contribution to an overview of gaseous pollution in African cities

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Qualidade de ar na área metropolitana do Porto, 1994-1999

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Arch. Environ. Contam. Toxicol.

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