Dataset Information

Nitrogen oxides (NO and NO₂) pollution in the Accra metropolis: Spatiotemporal patterns and the role of meteorology.

ABSTRACT: Economic and urban development in sub-Saharan Africa (SSA) may be shifting the dominant air pollution sources in cities from biomass to road traffic. Considered as a marker for traffic-related air pollution in cities, we conducted a city-wide measurement of NO_x levels in the Accra Metropolis and examined their spatiotemporal patterns in relation to land use and meteorological factors. Between April 2019 to June 2020, we collected weekly integrated NO_x (n = 428) and NO₂ (n = 472) samples at 10 fixed (year-long) and 124 rotating (week-long) sites. Data from the same time of year were compared to a previous study (2006) to assess changes in NO₂ concentrations. NO and NO₂ concentrations were highest in commercial/business/industrial (66 and 76 μg/m³, respectively) and high-density residential areas (47 and 59 μg/m³, respectively), compared with peri-urban locations. We observed annual means of 68 and 70 μg/m³ for NO and NO₂, and a clear seasonal variation, with the mean NO₂ of 63 μg/m³ (non-Harmattan) increased by 25-56% to 87 μg/m³ (Harmattan) across different site types. The NO₂/NO_x ratio was also elevated by 1928%. Both NO and NO₂ levels were associated with indicators of road traffic emissions (e.g. distance to major roads), but not with community biomass use (e.g. wood and charcoal). We found strong correlations between both NO₂ and NO₂/NO_x and mixing layer depth, incident solar radiation and water vapor mixing ratio. These findings represent an increase of 25-180% when compared to a small study conducted in two high-density residential neighborhoods in Accra in 2006. Road traffic may be replacing community biomass use (major source of fine particulate matter) as the prominent source of air pollution in Accra, with policy implication for growing cities in SSA.

SUBMITTER: Wang J

PROVIDER: S-EPMC7611659 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:IntroductionAir and noise pollution are emerging environmental health hazards in African cities, with potentially complex spatial and temporal patterns. Limited local data are a barrier to the formulation and evaluation of policies to reduce air and noise pollution.Methods and analysisWe designed a year-long measurement campaign to characterise air and noise pollution and their sources at high-resolution within the Greater Accra Metropolitan Area (GAMA), Ghana. Our design uses a combination of fixed (year-long, n=10) and rotating (week-long, n =~130) sites, selected to represent a range of land uses and source influences (eg, background, road traffic, commercial, industrial and residential areas, and various neighbourhood socioeconomic classes). We will collect data on fine particulate matter (PM2.5), nitrogen oxides (NOx), weather variables, sound (noise level and audio) along with street-level time-lapse images. We deploy low-cost, low-power, lightweight monitoring devices that are robust, socially unobtrusive, and able to function in Sub-Saharan African (SSA) climate. We will use state-of-the-art methods, including spatial statistics, deep/machine learning, and processed-based emissions modelling, to capture highly resolved temporal and spatial variations in pollution levels across the GAMA and to identify their potential sources. This protocol can serve as a prototype for other SSA cities.Ethics and disseminationThis environmental study was deemed exempt from full ethics review at Imperial College London and the University of Massachusetts Amherst; it was approved by the University of Ghana Ethics Committee (ECH 149/18-19). This protocol is designed to be implementable in SSA cities to map environmental pollution to inform urban planning decisions to reduce health harming exposures to air and noise pollution. It will be disseminated through local stakeholder engagement (public and private sectors), peer-reviewed publications, contribution to policy documents, media, and conference presentations.

Project description:As a pandemic hotspot in Japan, between March 1, 2020-October 1, 2022, Tokyo metropolis experienced seven COVID-19 waves. Motivated by the high rate of COVID-19 incidence and mortality during the seventh wave, and environmental/health challenges we conducted a time-series analysis to investigate the long-term interaction of air quality and climate variability with viral pandemic in Tokyo. Through daily time series geospatial and observational air pollution/climate data, and COVID-19 incidence and death cases, this study compared the environmental conditions during COVID-19 multiwaves. In spite of five State of Emergency (SOEs) restrictions associated with COVID-19 pandemic, during (2020-2022) period air quality recorded low improvements relative to (2015-2019) average annual values, namely: Aerosol Optical Depth increased by 9.13% in 2020 year, and declined by 6.64% in 2021, and 12.03% in 2022; particulate matter PM2.5 and PM10 decreased during 2020, 2021, and 2022 years by 10.22%, 62.26%, 0.39%, and respectively by 4.42%, 3.95%, 5.76%. For (2021-2022) period the average ratio of PM2.5/PM10 was (0.319 ± 0.1640), showing a higher contribution to aerosol loading of traffic-related coarse particles in comparison with fine particles. The highest rates of the daily recorded COVID-19 incidence and death cases in Tokyo during the seventh COVID-19 wave (1 July 2022-1 October 2022) may be attributed to accumulation near the ground of high levels of air pollutants and viral pathogens due to: 1) peculiar persistent atmospheric anticyclonic circulation with strong positive anomalies of geopotential height at 500 hPa; 2) lower levels of Planetary Boundary Layer (PBL) heights; 3) high daily maximum air temperature and land surface temperature due to the prolonged heat waves (HWs) in summer 2022; 4) no imposed restrictions. Such findings can guide public decision-makers to design proper strategies to curb pandemics under persistent stable anticyclonic weather conditions and summer HWs in large metropolitan areas.

Project description:BACKGROUND:Accurate estimation of nitrogen dioxide (NO2) and nitrogen oxide (NOx) concentrations at high spatiotemporal resolutions is crucial for improving evaluation of their health effects, particularly with respect to short-term exposures and acute health outcomes. For estimation over large regions like California, high spatial density field campaign measurements can be combined with more sparse routine monitoring network measurements to capture spatiotemporal variability of NO2 and NOx concentrations. However, monitors in spatially dense field sampling are often highly clustered and their uneven distribution creates a challenge for such combined use. Furthermore, heterogeneities due to seasonal patterns of meteorology and source mixtures between sub-regions (e.g. southern vs. northern California) need to be addressed. OBJECTIVES:In this study, we aim to develop highly accurate and adaptive machine learning models to predict high-resolution NO2 and NOx concentrations over large geographic regions using measurements from different sources that contain samples with heterogeneous spatiotemporal distributions and clustering patterns. METHODS:We used a comprehensive Kruskal-K-means method to cluster the measurement samples from multiple heterogeneous sources. Spatiotemporal cluster-based bootstrap aggregating (bagging) of the base mixed-effects models was then applied, leveraging the clusters to obtain balanced and less correlated training samples for less bias and improvement in generalization. Further, we used the machine learning technique of grid search to find the optimal interaction of temporal basis functions and the scale of spatial effects, which, together with spatiotemporal covariates, adequately captured spatiotemporal variability in NO2 and NOx at the state and local levels. RESULTS:We found an optimal combination of four temporal basis functions and 200?m scale spatial effects for the base mixed-effects models. With the cluster-based bagging of the base models, we obtained robust predictions with an ensemble cross validation R2 of 0.88 for both NO2 and NOx [RMSE (RMSEIQR): 3.62?ppb (0.28) and 9.63?ppb (0.37) respectively]. In independent tests of random sampling, our models achieved similarly strong performance (R2 of 0.87-0.90; RMSE of 3.97-9.69?ppb; RMSEIQR of 0.21-0.27), illustrating minimal over-fitting. CONCLUSIONS:Our approach has important implications for fusing data from highly clustered and heterogeneous measurement samples from multiple data sources to produce highly accurate concentration estimates of air pollutants such as NO2 and NOx at high resolution over a large region.

Project description:Background:Many young people have a tendency to be concerned about their physical appearance and undertake practices in order to achieve certain body ideals. There is however limited information from developing countries on the weight perceptions of emerging adults (i.e. individuals leaving the adolescence life stage and preparing to take on adulthood) and whether these opinions influence their nutritional status and weight management practices. This study sought to assess emerging adults' nutritional status, their weight perceptions and the methods they use to manage their weight. Methods:This study was cross-sectional, involving emerging adults (N?=?192) recruited at shopping areas in the Accra Metropolis of Ghana. A pretested questionnaire was used to collect information on demographic characteristics, weight perceptions, and weight management strategies. Anthropometric measurements were taken using standard procedures. Descriptive analysis was performed on the demographic data, methods used to manage weight, and weight perceptions. Logistic regression was used to assess possible relationships between weight perceptions and nutritional status as well as weight perceptions and weight management practices. Results:The mean age of participants was 21.8(2.2) years with 51.0% of participants being female. Majority of the participants perceived normal weight status as the ideal body for themselves and half of them thought that they were slimmer than they actually were in reality. Three major weight management strategies were identified: engaging in physical activity, dieting and making lifestyle modifications (i.e. changes in normal eating habits coupled with regular physical activity and behavioral changes). Emerging adults who had an inaccurate body image perception were less likely (OR?=?0.30, 95% CI: 0.15-0.61) to have a healthy nutritional status than emerging adults who had an accurate body image perception. Conclusion:Weight perception was associated with nutritional status. Discussions with nutrition professionals regarding realistic weight ideals would be beneficial for this age-group since half of the study's participants had inaccurate perceptions about their current weight statuses even though their statuses were normal.

Dataset Information

Nitrogen oxides (NO and NO₂) pollution in the Accra metropolis: Spatiotemporal patterns and the role of meteorology.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

Nitrogen oxides (NO and NO2) pollution in the Accra metropolis: Spatiotemporal patterns and the role of meteorology.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Nitrogen oxides (NO and NO₂) pollution in the Accra metropolis: Spatiotemporal patterns and the role of meteorology.