Spatial and Temporal Variations in PM10 Concentrations between 2010-2017 in South Africa.
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ABSTRACT:
SUBMITTER: Arowosegbe OO
PROVIDER: S-EPMC8706960 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Good quality and completeness of ambient air quality monitoring data is central in supporting actions towards mitigating the impact of ambient air pollution. In South Africa, however, availability of continuous ground-level air pollution monitoring data is scarce and incomplete. To address this issue, we developed and compared different modeling approaches to impute missing daily average particulate matter (PM10) data between 2010 and 2017 using spatiotemporal predictor variables. The random forest (RF) machine learning method was used to explore the relationship between average daily PM10 concentrations and spatiotemporal predictors like meteorological, land use and source-related variables. National (8 models), provincial (32) and site-specific (44) RF models were developed to impute missing daily PM10 data. The annual national, provincial and site-specific RF cross-validation (CV) models explained on average 78%, 70% and 55% of ground-level PM10 concentrations, respectively. The spatial components of the national and provincial CV RF models explained on average 22% and 48%, while the temporal components of the national, provincial and site-specific CV RF models explained on average 78%, 68% and 57% of ground-level PM10 concentrations, respectively. This study demonstrates a feasible approach based on RF to impute missing measurement data in areas where data collection is sparse and incomplete.
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Project description:Air pollution both indoors and outdoors is a major cause of various diseases and premature deaths. Negative health effects are more frequently observed in a number of European countries characterized by significant pollution. In Poland, especially in Upper Silesia, the most serious problem is the high concentration of particulate matter (PM) and PM10-bound benzo[a]pyrene (B[a]P). The main source of these two pollutants is so-called "low emissions" associated with the burning of solid fuels mainly in domestic boilers and liquid fuels in road traffic. This study examined the variability in the PM and PM10-bound B[a]P concentrations and their relationships with meteorological parameters, i.e., atmospheric pressure, air temperature and wind speed, in 2018-2021 at 11 monitoring stations. In many Silesian cities, the average annual concentrations of PM10, PM2.5 and B[a]P were much higher than those recorded in other European countries. At each station, the average daily PM10 concentrations were exceeded on 12 to 126 days a year. Taking into account the WHO recommendation for PM2.5, the highest recorded average daily concentration exceeded the permissible level by almost 40 times. The same relationships were observed in all measurement years: PM10 concentrations were negatively correlated with air temperature (R = -0.386) and wind speed (R = -0.614). The highest concentrations were observed in the temperature range from -15 °C to -5 °C, when the wind speed did not exceed 0.5 m·s-1. The calculated lifetime cancer risk (LCR) associated with the exposure to B[a]P in the Silesian Voivodeship suggested 30-429 cases per 1 million people in the heating season depending on the scenario used for the calculations (IRIS, EPA or WHO).
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Project description:BackgroundCardiovascular disease (CVD) is the leading cause of mortality worldwide and in Colombia. The analysis of CVD mortality has been mainly relied on individual factors and rates, but occurrence is also related to contextual conditions. Understanding the distribution of CVD in a region will contribute to implement more focused-preventive and care interventions.MethodsUsing the national mortality register established by the Department of National Statistics, standardized rates and spatial distribution of CVD mortality were estimated for Cali, Colombia, between 2010-2017. Global and local spatial aggregation was assessed using the Geary's C test and for each district standardized mortality ratios smoothed by the Bayesian empirical were estimated.ResultsOver the period, CVD was the main cause of mortality with 28,804 deaths accounting for 23,8% of total deaths. The global CVD mortality rate varied from 235.9 to 257.4 per 100.000 habitants, with an average increase of 9.1% in the percentage change. The main cause of mortality were hypertensive diseases following by ischemic heart diseases and stroke. The standardized mortality ratios smoothed by the Bayesian empirical method showed that the districts 7, 13, 14, 15 and 16 located at the eastern area with the lowest socio-economic strata and the district 22 at the south of the city with the highest socio-economic strata had the high risks of CVD mortality. All these areas were at the boundary of the city. The the lowest risk was observed in districts 1 and 2 at the northwest area with the upper socio-economic strata. Over the study period, a spatial autocorrelation was found in the districts 1,9 10, 11, 12, 13, 14, 15, 19, and 21 by using the Geary's C test. The highest significant spatial association was found in the districts 1 and 21.ConclusionOf 22 districts in Cali, the highest risk of CVD mortality was found in three at the lowest and one in the upper socio-economic strata between 2013 and 2017. Over the period a global spatial aggregation was identified due mainly to districts peripherical located suggesting that there could be contextual conditions influencing the risk. Therefore, there is a need for considering local conditions to prevent CVD mortality.
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Project description:We investigated temporal variations of ambient air pollutants and the influences of meteorological parameters on their concentrations using a robust method; convergent cross mapping; in Tehran (2012-2017). Tehran citizens were consistently exposed to annual PM2.5, PM10 and NO2 approximately 3.0-4.5, 3.5-4.5 and 1.5-2.5 times higher than the World Health Organization air quality guideline levels during the period. Except for O3, all air pollutants demonstrated the lowest and highest concentrations in summertime and wintertime, respectively. The highest O3 concentrations were found on weekend (weekend effect), whereas other ambient air pollutants had statistically significant (P < 0.05) daily variations in which higher concentrations were observed on weekdays compared to weekend (holiday effect). Hourly O3 concentration reached its peak at 3.00 p.m., though other air pollutants displayed two peaks; morning and late night. Approximately 45% to 65% of AQI values were in the subcategory of unhealthy for sensitive groups and PM2.5 was the responsible air pollutant in Tehran. Amongst meteorological factors, temperature was the key influencing factor for PM2.5 and PM10 concentrations, while nebulosity and solar radiation exerted major influences on ambient SO2 and O3 concentrations. Additionally, there is a moderate coupling between wind speed and NO2 and CO concentrations.
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Project description:Air pollution has aroused significant public concern in China, therefore, long-term air-quality data with high temporal and spatial resolution are needed to understand the variations of air pollution in China. However, the yearly variations with high spatial resolution of air quality and six air pollutants are still unknown for China until now. Therefore, in this paper, we analyze the spatial and temporal variations of air quality and six air pollutants in 366 cities across mainland China during 2015-2017 for the first time to the best of our knowledge. The results indicate that the annual mean mass concentrations of PM2.5, PM10, SO2, and CO all decreased year by year during 2015-2017. However, the annual mean NO2 concentrations were almost unchanged, while the annual mean O3 concentrations increased year by year. Anthropogenic factors were mainly responsible for the variations of air quality. Further analysis suggested that PM2.5 and PM10 were the main factors influencing air quality, while NO2 played an important role in the formation of PM2.5 and O3. These findings can provide a theoretical basis for the formulation of future air-pollution control policy in China.
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Project description:With the increase in anthropogenic activities metal pollution is also increased and needs to be closely monitored. In this study honeybees were used as bioindicators to monitor metal pollution. Metal pollution in honeybees represents pollution present in air, water and soil. Concentrations of As, Cs, Hg, Mo, Sb, Se, U and V were measured. The aim of this study was to assess spatial and temporal variations of metal concentrations in honeybees. Samples of honeybees were taken at five different regions in Serbia (Belgrade - BG, Pan?evo - PA, Pavliš - PV, Mesi? - MS, and Kostolac - TPP) during 2014. Spatial variations were observed for Sb, which had higher concentrations in BG compared to all other regions, and for U, with higher concentrations in the TPP region. High concentrations of Sb in BG were attributed to intense traffic, while higher U concentrations in the TPP region are due to the vicinity of coal fired power plants. In order to assess temporal variations at two locations (PA and PV) samples were taken during July and September of 2014 and June, July, August and September of 2015. During 2014 observing months of sampling higher concentrations in July were detected for Sb and U in BG, which is attributed to lifecycle of plants and honeybees. During the same year higher concentrations in September were observed for As, Sb in PA and Hg in PV. This is due to high precipitation during the peak of bee activity in spring/summer of 2014. No differences between months of sampling were detected during 2015. Between 2014 and 2015 statistically significant differences were observed for Hg, Mo and V; all elements had higher concentrations in 2014. This is in accordance with the trend of reduction of metal concentrations in the bodies of honeybees throughout the years in this region.
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Project description:Climate change impacts on maize production in South Africa, i.e., interannual yield variabilities, are still not well understood. This study is based on a recently released reanalysis of climate observations (AgERA5), i.e., temperature, precipitation, solar radiation, and wind speed data. The study assesses climate change effects by quantifying the trend of agrometeorological indicators, their correlation with maize yield, and analyzing their spatiotemporal patterns using Empirical Orthogonal Function. Thereby, the main agrometeorological factors that affected yield variability for the last 31 years (1990/91-2020/21 growing season) in major maize production provinces, namely Free State, KwaZulu-Natal, Mpumalanga, and North West are identified. Results show that there was a significant positive trend in temperature that averages 0.03-0.04 °C per year and 0.02-0.04 °C per growing season. There was a decreasing trend in precipitation in Free State with 0.01 mm per year. Solar radiation did not show a significant trend. Wind speed in Free State increased at a rate of 0.01 ms-1 per growing season. Yield variabilities in Free State, Mpumalanga, and North West show a significant positive correlation (r > 0.43) with agrometeorological variables. Yield in KwaZulu-Natal is not influenced by climate factors. The leading mode (50-80% of total variance) of each agrometeorological variable indicates spatially homogenous pattern across the regions. The dipole patterns of the second and the third mode suggest the variabilities of agrometeorological indicators are linked to South Indian high pressure and the warm Agulhas current. The corresponding principal components were mainly associated with strong climate anomalies which are identified as El Niño and La Niña events.
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