International Journal of Environmental Research and Public Health

Research

17 pages, 3699 KiB

Open AccessArticle

Machine Learning Prediction Model of Tuberculosis Incidence Based on Meteorological Factors and Air Pollutants

by Na Tang, Maoxiang Yuan, Zhijun Chen, Jian Ma, Rui Sun, Yide Yang, Quanyuan He, Xiaowei Guo, Shixiong Hu and Junhua Zhou

Int. J. Environ. Res. Public Health 2023, 20(5), 3910; https://doi.org/10.3390/ijerph20053910 - 22 Feb 2023

Cited by 5 | Viewed by 2552

Abstract

Background: Tuberculosis (TB) is a public health problem worldwide, and the influence of meteorological and air pollutants on the incidence of tuberculosis have been attracting interest from researchers. It is of great importance to use machine learning to build a prediction model of [...] Read more.

Background: Tuberculosis (TB) is a public health problem worldwide, and the influence of meteorological and air pollutants on the incidence of tuberculosis have been attracting interest from researchers. It is of great importance to use machine learning to build a prediction model of tuberculosis incidence influenced by meteorological and air pollutants for timely and applicable measures of both prevention and control. Methods: The data of daily TB notifications, meteorological factors and air pollutants in Changde City, Hunan Province ranging from 2010 to 2021 were collected. Spearman rank correlation analysis was conducted to analyze the correlation between the daily TB notifications and the meteorological factors or air pollutants. Based on the correlation analysis results, machine learning methods, including support vector regression, random forest regression and a BP neural network model, were utilized to construct the incidence prediction model of tuberculosis. RMSE, MAE and MAPE were performed to evaluate the constructed model for selecting the best prediction model. Results: (1) From the year 2010 to 2021, the overall incidence of tuberculosis in Changde City showed a downward trend. (2) The daily TB notifications was positively correlated with average temperature (r = 0.231), maximum temperature (r = 0.194), minimum temperature (r = 0.165), sunshine duration (r = 0.329), PM_2.5 (r = 0.097), PM₁₀ (r = 0.215) and O₃ (r = 0.084) (p < 0.05). However, there was a significant negative correlation between the daily TB notifications and mean air pressure (r = −0.119), precipitation (r = −0.063), relative humidity (r = −0.084), CO (r = −0.038) and SO₂ (r = −0.034) (p < 0.05). (3) The random forest regression model had the best fitting effect, while the BP neural network model exhibited the best prediction. (4) The validation set of the BP neural network model, including average daily temperature, sunshine hours and PM₁₀, showed the lowest root mean square error, mean absolute error and mean absolute percentage error, followed by support vector regression. Conclusions: The prediction trend of the BP neural network model, including average daily temperature, sunshine hours and PM₁₀, successfully mimics the actual incidence, and the peak incidence highly coincides with the actual aggregation time, with a high accuracy and a minimum error. Taken together, these data suggest that the BP neural network model can predict the incidence trend of tuberculosis in Changde City. Full article

(This article belongs to the Special Issue Advances in Air Pollution Meteorology Research)

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19 pages, 3620 KiB

Open AccessArticle

A Study of Urban Haze and Its Association with Cold Surge and Sea Breeze for Greater Bangkok

by Nishit Aman, Kasemsan Manomaiphiboon, Natchanok Pala-En, Bikash Devkota, Muanfun Inerb and Eakkachai Kokkaew

Int. J. Environ. Res. Public Health 2023, 20(4), 3482; https://doi.org/10.3390/ijerph20043482 - 16 Feb 2023

Cited by 3 | Viewed by 2078

Abstract

This study deals with haze characteristics under the influence of the cold surge and sea breeze for Greater Bangkok (GBK) in 2017–2022, including haze intensity and duration, meteorological classification for haze, and the potential effects of secondary aerosols and biomass burning. A total [...] Read more.

This study deals with haze characteristics under the influence of the cold surge and sea breeze for Greater Bangkok (GBK) in 2017–2022, including haze intensity and duration, meteorological classification for haze, and the potential effects of secondary aerosols and biomass burning. A total of 38 haze episodes and 159 haze days were identified. The episode duration varies from a single day to up to 14 days, suggesting different pathways of its formation and evolution. Short-duration episodes of 1–2 days are the most frequent with 18 episodes, and the frequency of haze episodes decreases as the haze duration increases. The increase in complexity in the formation of relatively longer episodes is suggested by a relatively higher coefficient of variation for PM_2.5. Four meteorology-based types of haze episodes were classified. Type I is caused by the arrival of the cold surge in GBK, which leads to the development of stagnant conditions favorable for haze formation. Type II is induced by sea breeze, which leads to the accumulation of air pollutants due to its local recirculation and development of the thermal internal boundary layer. Type III consists of the haze episodes caused by the synergetic effect of the cold surge and sea breeze while Type IV consists of short haze episodes that are not affected by either the cold surge or sea breeze. Type II is the most frequent (15 episodes), while Type III is the most persistent and most polluted haze type. The spread of haze or region of relatively higher aerosol optical depth outside GBK in Type III is potentially due to advection and dispersion, while that in Type IV is due to short 1-day episodes potentially affected by biomass burning. Due to cold surge, the coolest and driest weather condition is found under Type I, while Type II has the most humid condition and highest recirculation factor due to the highest average sea breeze duration and penetration. The precursor ratio method suggests the potential effect of secondary aerosols on 34% of the total haze episodes. Additionally, biomass burning is found to potentially affect half of the total episodes as suggested by the examination of back trajectories and fire hotspots. Based on these results, some policy implications and future work are also suggested. Full article

(This article belongs to the Special Issue Advances in Air Pollution Meteorology Research)

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20 pages, 13602 KiB

Open AccessArticle

Spatiotemporal Analysis of Black Carbon Sources: Case of Santiago, Chile, under SARS-CoV-2 Lockdowns

by Carla Adasme, Ana María Villalobos and Héctor Jorquera

Int. J. Environ. Res. Public Health 2022, 19(24), 17064; https://doi.org/10.3390/ijerph192417064 - 19 Dec 2022

Cited by 3 | Viewed by 1542

Abstract

Background: The SARS-CoV-2 pandemic has temporarily decreased black carbon emissions worldwide. The use of multi-wavelength aethalometers provides a quantitative apportionment of black carbon (BC) from fossil fuels (BC_ff) and wood-burning sources (BC_wb). However, this apportionment is aggregated: local and [...] Read more.

Background: The SARS-CoV-2 pandemic has temporarily decreased black carbon emissions worldwide. The use of multi-wavelength aethalometers provides a quantitative apportionment of black carbon (BC) from fossil fuels (BC_ff) and wood-burning sources (BC_wb). However, this apportionment is aggregated: local and non-local BC sources are lumped together in the aethalometer results. Methods: We propose a spatiotemporal analysis of BC results along with meteorological data, using a fuzzy clustering approach, to resolve local and non-local BC contributions. We apply this methodology to BC measurements taken at an urban site in Santiago, Chile, from March through December 2020, including lockdown periods of different intensities. Results: BC_ff accounts for 85% of total BC; there was up to an 80% reduction in total BC during the most restrictive lockdowns (April–June); the reduction was 40–50% in periods with less restrictive lockdowns. The new methodology can apportion BC_ff and BC_wb into local and non-local contributions; local traffic (wood burning) sources account for 66% (86%) of BC_ff (BC_wb). Conclusions: The intensive lockdowns brought down ambient BC across the city. The proposed fuzzy clustering methodology can resolve local and non-local contributions to BC in urban zones. Full article

(This article belongs to the Special Issue Advances in Air Pollution Meteorology Research)

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22 pages, 7960 KiB

Open AccessArticle

Artificial Neural Network Modeling on PM₁₀, PM_2.5, and NO₂ Concentrations between Two Megacities without a Lockdown in Korea, for the COVID-19 Pandemic Period of 2020

by Soo-Min Choi and Hyo Choi

Int. J. Environ. Res. Public Health 2022, 19(23), 16338; https://doi.org/10.3390/ijerph192316338 - 6 Dec 2022

Cited by 6 | Viewed by 1831

Abstract

The mutual relationship among daily averaged PM₁₀, PM_2.5, and NO₂ concentrations in two megacities (Seoul and Busan) connected by the busiest highway in Korea was investigated using an artificial neural network model (ANN)-sigmoid function, for a novel coronavirus [...] Read more.

The mutual relationship among daily averaged PM₁₀, PM_2.5, and NO₂ concentrations in two megacities (Seoul and Busan) connected by the busiest highway in Korea was investigated using an artificial neural network model (ANN)-sigmoid function, for a novel coronavirus (COVID-19) pandemic period from 1 January to 31 December 2020. Daily and weekly mean concentrations of NO₂ in 2020 under neither locked down cities, nor limitation of the activities of vehicles and people by the Korean Government have decreased by about 15%, and 12% in Seoul, and Busan cities, than the ones in 2019, respectively. PM ₁₀ (PM_2.5) concentration has also decreased by 15% (10%), and 12% (10%) in Seoul, and Busan, with a similar decline of NO₂, causing an improvement in air quality in each city. Multilayer perception (MLP), which has a back-propagation training algorithm for a feed-forward artificial neural network technique with a sigmoid activation function was adopted to predict daily averaged PM₁₀, PM_2.5, and NO₂ concentrations in two cities with their interplay. Root mean square error (RMSE) with the coefficient of determination (R²) evaluates the performance of the model between the predicted and measured values of daily mean PM₁₀, PM_2.5, and NO_2, in Seoul were 2.251 with 0.882 (1.909 with 0.896; 1.913 with 0.892), 0.717 with 0.925 (0.955 with 0.930; 0.955 with 0.922), and 3.502 with 0.729 (2.808 with 0.746; 3.481 with 0.734), in 2 (5; 7) nodes in a single hidden layer. Similarly, they in Busan were 2.155 with 0.853 (1.519 with 0.896; 1.649 with 0.869), 0.692 with 0.914 (0.891 with 0.910; 1.211 with 0.883), and 2.747 with 0.667 (2.277 with 0.669; 2.137 with 0.689), respectively. The closeness of the predicted values to the observed ones shows a very high Pearson r correlation coefficient of over 0.932, except for 0.818 of NO₂ in Busan. Modeling performance using IBM SPSS-v27 software on daily averaged PM₁₀, PM_2.5, and NO₂ concentrations in each city were compared by scatter plots and their daily distributions between predicted and observed values. Full article

(This article belongs to the Special Issue Advances in Air Pollution Meteorology Research)

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12 pages, 3150 KiB

Open AccessArticle

16 Years (2006–2021) of Surface Ozone Measurements in Córdoba (Southern Spain): Trends and the Impact of the COVID-19 Lockdown

by Miguel A. Hernández-Ceballos, Alberto Jiménez-Solano and Julio Torres-Fernández

Int. J. Environ. Res. Public Health 2022, 19(23), 16210; https://doi.org/10.3390/ijerph192316210 - 3 Dec 2022

Cited by 1 | Viewed by 1465

Abstract

Surface ozone concentrations (O₃) during the period 2006–2021 are analysed at Córdoba city (southern Iberian Peninsula) in suburban and urban sampling sites. The aims are to present the levels and temporal variations, to explore trends and to quantity the variation in [...] Read more.

Surface ozone concentrations (O₃) during the period 2006–2021 are analysed at Córdoba city (southern Iberian Peninsula) in suburban and urban sampling sites. The aims are to present the levels and temporal variations, to explore trends and to quantity the variation in O₃ concentrations in the context of the COVID-19 lockdown. The O₃ means are higher in the suburban station (62 µg m⁻³ and 51.3 µg m⁻³), being the information level threshold only exceeded twice during this period. The daily evolution shows a maximum at about 17:00 UTC, whereas the minimum is reached at about 9:00 UTC, with higher levels in the suburban station. The seasonal evolution of this daily cycle also presents monthly differences in shape and intensity between stations. The trends are analysed by means of daily averages and daily 5th and 95th percentiles, and they show a similar increase in all of these parameters, with special emphasis on the daily P95 concentrations, with 0.27 µg m⁻³ year⁻¹ and 0.24 µg m⁻³ year⁻¹. Finally, the impact of the COVID-19 lockdown shows a decline in O₃ concentrations over 10%. Full article

(This article belongs to the Special Issue Advances in Air Pollution Meteorology Research)

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22 pages, 7176 KiB

Open AccessArticle

Analysis of Vertical Distribution Changes and Influencing Factors of Tropospheric Ozone in China from 2005 to 2020 Based on Multi-Source Data

by Yong Zhang, Yang Zhang, Zhihong Liu, Sijia Bi and Yuni Zheng

Int. J. Environ. Res. Public Health 2022, 19(19), 12653; https://doi.org/10.3390/ijerph191912653 - 3 Oct 2022

Cited by 1 | Viewed by 1651

Abstract

The vertical distribution of the tropospheric ozone column concentration (OCC) in China from 2005 to 2020 was analysed based on the ozone profile product of the ozone monitoring instrument (OMI). The annual average OCC in the lower troposphere (OCC_LT) showed an [...] Read more.

The vertical distribution of the tropospheric ozone column concentration (OCC) in China from 2005 to 2020 was analysed based on the ozone profile product of the ozone monitoring instrument (OMI). The annual average OCC in the lower troposphere (OCC_LT) showed an increasing trend, with an average annual increase of 0.143 DU. The OCC in the middle troposphere showed a downward trend, with an average annual decrease of 0.091 DU. There was a significant negative correlation between the ozone changes in the two layers. The monthly average results show that the peak values of OCC_LT occur in May or June, the middle troposphere is significantly influenced by topographic conditions, and the upper troposphere is mainly affected by latitude. Analysis based on multi-source data shows that the reduction in nitrogen oxides (NO_x) and the increase in volatile organic compounds (VOCs) weakened the titration of ozone generation, resulting in the increase in OCC_LT. The increase in vegetation is closely related to the increase in OCC_LT, with a correlation coefficient of up to 0.875. The near-surface temperature increased significantly, which strengthened the photochemical reaction of ozone. In addition, the increase in boundary layer height also plays a positive role in the increase in OCC_LT. Full article

(This article belongs to the Special Issue Advances in Air Pollution Meteorology Research)

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14 pages, 17803 KiB

Open AccessArticle

PM_2.5 Concentrations Variability in North China Explored with a Multi-Scale Spatial Random Effect Model

by Hang Zhang, Yong Liu, Dongyang Yang and Guanpeng Dong

Int. J. Environ. Res. Public Health 2022, 19(17), 10811; https://doi.org/10.3390/ijerph191710811 - 30 Aug 2022

Cited by 3 | Viewed by 1469

Abstract

Compiling fine-resolution geospatial PM_2.5 concentrations data is essential for precisely assessing the health risks of PM_2.5 pollution exposure as well as for evaluating environmental policy effectiveness. In most previous studies, global and local spatial heterogeneity of PM_2.5 is captured by [...] Read more.

Compiling fine-resolution geospatial PM_2.5 concentrations data is essential for precisely assessing the health risks of PM_2.5 pollution exposure as well as for evaluating environmental policy effectiveness. In most previous studies, global and local spatial heterogeneity of PM_2.5 is captured by the inclusion of multi-scale covariate effects, while the modelling of genuine scale-dependent variabilities pertaining to the spatial random process of PM_2.5 has not yet been much studied. Consequently, this work proposed a multi-scale spatial random effect model (MSSREM), based a recently developed fixed-rank Kriging method, to capture both the scale-dependent variabilities and the spatial dependence effect simultaneously. Furthermore, a small-scale Monte Carlo simulation experiment was conducted to assess the performance of MSSREM against classic geospatial Kriging models. The key results indicated that when the multiple-scale property of local spatial variabilities were exhibited, the MSSREM had greater ability to recover local- or fine-scale variations hidden in a real spatial process. The methodology was applied to the PM_2.5 concentrations modelling in North China, a region with the worst air quality in the country. The MSSREM provided high prediction accuracy, 0.917 R-squared, and 3.777 root mean square error (RMSE). In addition, the spatial correlations in PM_2.5 concentrations were properly captured by the model as indicated by a statistically insignificant Moran’s I statistic (a value of 0.136 with p-value > 0.2). Overall, this study offers another spatial statistical model for investigating and predicting PM_2.5 concentration, which would be beneficial for precise health risk assessment of PM_2.5 pollution exposure. Full article

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32 pages, 9101 KiB

Open AccessArticle

Development and Evaluation of Statistical Models Based on Machine Learning Techniques for Estimating Particulate Matter (PM_2.5 and PM₁₀) Concentrations

by Wan Yun Hong, David Koh and Liya E. Yu

Int. J. Environ. Res. Public Health 2022, 19(13), 7728; https://doi.org/10.3390/ijerph19137728 - 23 Jun 2022

Cited by 5 | Viewed by 2046

Abstract

Despite extensive research on air pollution estimation/prediction, inter-country models for estimating air pollutant concentrations in Southeast Asia have not yet been fully developed and validated owing to the lack of air quality (AQ), emission inventory and meteorological data from different countries in the [...] Read more.

Despite extensive research on air pollution estimation/prediction, inter-country models for estimating air pollutant concentrations in Southeast Asia have not yet been fully developed and validated owing to the lack of air quality (AQ), emission inventory and meteorological data from different countries in the region. The purpose of this study is to develop and evaluate two machine learning (ML)-based models (i.e., analysis of covariance (ANCOVA) and random forest regression (RFR)) for estimating daily PM_2.5 and PM₁₀ concentrations in Brunei Darussalam. These models were first derived from past AQ and meteorological measurements in Singapore and then tested with AQ and meteorological data from Brunei Darussalam. The results show that the ANCOVA model (

R^{2}

= 0.94 and RMSE = 0.05 µg/m³ for PM_2.5, and

R^{2}

= 0.72 and RMSE = 0.09 µg/m³ for PM₁₀) could describe daily PM concentrations over 18 µg/m³ in Brunei Darussalam much better than the RFR model (

R^{2}

= 0.92 and RMSE = 0.04 µg/m³ for PM_2.5, and

R^{2}

= 0.86 and RMSE = 0.08 µg/m³ for PM₁₀). In conclusion, the derived models provide a satisfactory estimation of PM concentrations for both countries despite some limitations. This study shows the potential of the models for inter-country PM estimations in Southeast Asia. Full article

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23 pages, 8989 KiB

Open AccessArticle

Multi-Year Variation of Ozone and Particulate Matter in Northeast China Based on the Tracking Air Pollution in China (TAP) Data

by Hujia Zhao, Ke Gui, Yanjun Ma, Yangfeng Wang, Yaqiang Wang, Hong Wang, Yu Zheng, Lei Li, Lei Zhang, Yuqi Zhang, Huizheng Che and Xiaoye Zhang

Int. J. Environ. Res. Public Health 2022, 19(7), 3830; https://doi.org/10.3390/ijerph19073830 - 23 Mar 2022

Cited by 13 | Viewed by 2421

Abstract

With the rapid development of economy and urbanization acceleration, ozone (O₃) pollution has become the main factor of urban air pollution in China after particulate matter. In this study, 90th percentile of maximum daily average (MDA) 8 h O₃ (O [...] Read more.

With the rapid development of economy and urbanization acceleration, ozone (O₃) pollution has become the main factor of urban air pollution in China after particulate matter. In this study, 90th percentile of maximum daily average (MDA) 8 h O₃ (O₃-8h-90per) and PM_2.5 data from the Tracking Air Pollution in China (TAP) dataset were used to determine the mean annual, seasonal, monthly, and interannual distribution of O₃-8h-90per and PM_2.5 concentrations in Northeast China (NEC). The O₃-8h-90per concentration was highest in Liaoning (>100 μg/m³), whereas the highest PM_2.5 concentration was observed mainly in urban areas of central Liaoning and the Harbin–Changchun urban agglomeration (approximately 60 μg/m³). The O₃-8h-90per concentrations were highest in spring and summer due to more intense solar radiation. On the contrary, the PM_2.5 concentration increased considerably in winter influenced by anthropogenic activities. In May and June, the highest monthly mean O₃-8h-90per concentrations were observed in central and western Liaoning, about 170–180 μg/m³, while the PM_2.5 concentrations were the highest in January, February, and December, approximately 100 μg/m³. The annual mean O₃-8h-90per concentration in NEC showed an increasing trend, while the PM_2.5 concentration exhibited an annual decline. By 2020, the annual mean O₃-8h-90per concentration in southern Liaoning had increased considerably, reaching 120–130 μg/m³. From the perspective of city levels, PM_2.5 and O₃-8h-90per also showed an opposite variation trend in the 35 cities of NEC. The reduced tropospheric NO₂ column is consistent with the decreasing trend of the interannual PM_2.5, while the increased surface temperature could be the main meteorological factor affecting the O₃-8h-90per concentration in NEC. The results of this study enable a comprehensive understanding of the regional and climatological O₃-8h-90per and PM_2.5 distribution at distinct spatial and temporal scales in NEC. Full article

(This article belongs to the Special Issue Advances in Air Pollution Meteorology Research)

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18 pages, 3538 KiB

Open AccessArticle

Meteorological Normalisation Using Boosted Regression Trees to Estimate the Impact of COVID-19 Restrictions on Air Quality Levels

by Sandra Ceballos-Santos, Jaime González-Pardo, David C. Carslaw, Ana Santurtún, Miguel Santibáñez and Ignacio Fernández-Olmo

Int. J. Environ. Res. Public Health 2021, 18(24), 13347; https://doi.org/10.3390/ijerph182413347 - 18 Dec 2021

Cited by 9 | Viewed by 4382

Abstract

The global COVID-19 pandemic that began in late December 2019 led to unprecedented lockdowns worldwide, providing a unique opportunity to investigate in detail the impacts of restricted anthropogenic emissions on air quality. A wide range of strategies and approaches exist to achieve this. [...] Read more.

The global COVID-19 pandemic that began in late December 2019 led to unprecedented lockdowns worldwide, providing a unique opportunity to investigate in detail the impacts of restricted anthropogenic emissions on air quality. A wide range of strategies and approaches exist to achieve this. In this paper, we use the “deweather” R package, based on Boosted Regression Tree (BRT) models, first to remove the influences of meteorology and emission trend patterns from NO, NO₂, PM₁₀ and O₃ data series, and then to calculate the relative changes in air pollutant levels in 2020 with respect to the previous seven years (2013–2019). Data from a northern Spanish region, Cantabria, with all types of monitoring stations (traffic, urban background, industrial and rural) were used, dividing the calendar year into eight periods according to the intensity of government restrictions. The results showed mean reductions in the lockdown period above −50% for NO_x, around −10% for PM₁₀ and below −5% for O₃. Small differences were found between the relative changes obtained from normalised data with respect to those from observations. These results highlight the importance of developing an integrated policy to reduce anthropogenic emissions and the need to move towards sustainable mobility to ensure safer air quality levels, as pre-existing concentrations in some cases exceed the safe threshold. Full article

(This article belongs to the Special Issue Advances in Air Pollution Meteorology Research)

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14 pages, 18768 KiB

Open AccessArticle

Contrasting Trends of Surface PM_2.5, O₃, and NO₂ and Their Relationships with Meteorological Parameters in Typical Coastal and Inland Cities in the Yangtze River Delta

by Min Lv, Zhanqing Li, Qingfeng Jiang, Tianmeng Chen, Yuying Wang, Anyong Hu, Maureen Cribb and Aling Cai

Int. J. Environ. Res. Public Health 2021, 18(23), 12471; https://doi.org/10.3390/ijerph182312471 - 26 Nov 2021

Cited by 16 | Viewed by 3002

Abstract

The contrasting trends of surface particulate matter (PM_2.5), ozone (O₃), and nitrogen dioxide (NO₂) and their relationships with meteorological parameters from 2015 to 2019 were investigated in the coastal city of Shanghai (SH) and the inland city [...] Read more.

The contrasting trends of surface particulate matter (PM_2.5), ozone (O₃), and nitrogen dioxide (NO₂) and their relationships with meteorological parameters from 2015 to 2019 were investigated in the coastal city of Shanghai (SH) and the inland city of Hefei (HF), located in the Yangtze River Delta (YRD). In both cities, PM_2.5 declined substantially, while O₃ and NO₂ showed peak values during 2017 when the most frequent extreme high-temperature events occurred. Wind speed was correlated most negatively with PM_2.5 and NO₂ concentrations, while surface temperature and relative humidity were most closely related to O₃. All of the studied pollutants were reduced by rainfall scavenging, with the greatest reduction seen in PM_2.5, followed by NO₂ and O₃. By contrast, air pollutants in the two cities were moderately strongly correlated, although PM_2.5 concentrations were much lower and O_x (O₃ + NO₂) concentrations were higher in SH. Additionally, complex air pollution hours occurred more frequently in SH. Air pollutant concentrations changed more with wind direction in SH. A more effective washout effect was observed in HF, likely due to the more frequent strong convection and thunderstorms in inland areas. This research suggests pertinent air quality control measures should be designed accordingly for specific geographical locations. Full article

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18 pages, 5620 KiB

Open AccessArticle

Modeling the Characteristics of Unhealthy Air Pollution Events: A Copula Approach

by Nurulkamal Masseran

Int. J. Environ. Res. Public Health 2021, 18(16), 8751; https://doi.org/10.3390/ijerph18168751 - 19 Aug 2021

Cited by 11 | Viewed by 2434

Abstract

This study proposes the concept of duration (D) and severity (S) measures, which were derived from unhealthy air pollution events. In parallel with that, the application of a copula model is proposed to evaluate unhealthy air pollution events with respect to their duration [...] Read more.

This study proposes the concept of duration (D) and severity (S) measures, which were derived from unhealthy air pollution events. In parallel with that, the application of a copula model is proposed to evaluate unhealthy air pollution events with respect to their duration and severity characteristics. The bivariate criteria represented by duration and severity indicate their structural dependency, long-tail, and non-identically marginal distributions. A copula approach can provide a good statistical tool to deal with these issues and enable the extraction of valuable information from air pollution data. Based on the copula model, several statistical measurements are proposed for describing the characteristics of unhealthy air pollution events, including the Kendall’s τ correlation of the copula, the conditional probability of air pollution severity based on a given duration, the joint OR/AND return period, and the conditional D|S and conditional S|D return periods. A case study based on air pollution data indices was conducted in Klang, Malaysia. The results indicate that a copula approach is beneficial for deriving valuable information for planning and mitigating the risks of unhealthy air pollution events. Full article

(This article belongs to the Special Issue Advances in Air Pollution Meteorology Research)

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16 pages, 2690 KiB

Open AccessArticle

Influence of Wind Speed on CO₂ and CH₄ Concentrations at a Rural Site

by Isidro A. Pérez, María de los Ángeles García, María Luisa Sánchez and Nuria Pardo

Int. J. Environ. Res. Public Health 2021, 18(16), 8397; https://doi.org/10.3390/ijerph18168397 - 9 Aug 2021

Cited by 6 | Viewed by 2089

Abstract

Meteorological variables have a noticeable impact on pollutant concentrations. Among these variables, wind speed is typically measured, although research into how pollutants respond to it can be improved. This study considers nine years of hourly CO₂ and CH₄ measurements at a [...] Read more.

Meteorological variables have a noticeable impact on pollutant concentrations. Among these variables, wind speed is typically measured, although research into how pollutants respond to it can be improved. This study considers nine years of hourly CO₂ and CH₄ measurements at a rural site, where wind speed values were calculated by the METEX model. Nine wind speed intervals are proposed where concentrations, distribution functions, and daily as well as annual cycles are calculated. Contrasts between local and transported concentrations are around 5 and 0.03 ppm for CO₂ and CH₄, respectively. Seven skewed distributions are applied, and five efficiency criteria are considered to test the goodness of fit, with the modified Nash–Sutcliffe efficiency proving to be the most sensitive statistic. The Gumbel distribution is seen to be the most suitable for CO₂, whereas the Weibull distribution is chosen for CH₄, with the exponential function being the worst. Finally, daily and annual cycles are analysed, where a gradual decrease in amplitude is observed, particularly for the daily cycle. Parametric and nonparametric procedures are used to fit both cycles. The latter gave the best fits, with the agreement being higher for the daily cycle, where evolution is smoother than for the annual cycle. Full article

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18 pages, 3841 KiB

Open AccessArticle

Qualitative Study on the Observations of Emissions, Transport, and the Influence of Climatic Factors from Sugarcane Burning: A South African Perspective

by Lerato Shikwambana, Xolile Ncipha, Sivakumar Kandasami Sangeetha, Venkataraman Sivakumar and Paidamwoyo Mhangara

Int. J. Environ. Res. Public Health 2021, 18(14), 7672; https://doi.org/10.3390/ijerph18147672 - 19 Jul 2021

Cited by 8 | Viewed by 3226

Abstract

There are two methods of harvesting sugarcane—manual or mechanical. Manual harvesting requires the burning of the standing sugarcane crop. Burning of the crop results in the emission of aerosols and harmful trace gases into the atmosphere. This work makes use of a long-term [...] Read more.

There are two methods of harvesting sugarcane—manual or mechanical. Manual harvesting requires the burning of the standing sugarcane crop. Burning of the crop results in the emission of aerosols and harmful trace gases into the atmosphere. This work makes use of a long-term dataset (1980–2019) to study (1) the atmospheric spatial and vertical distribution of pollutants; (2) the spatial distribution and temporal change of biomass emissions; and (3) the impact/influence of climatic factors on temporal change in atmospheric pollutant loading and biomass emissions over the Mpumalanga and KwaZulu Natal provinces in South Africa, where sugarcane farming is rife. Black carbon (BC) and sulfur dioxide (SO₂) are two dominant pollutants in the JJA and SON seasons due to sugarcane burning. Overall, there was an increasing trend in the emissions of BC, SO₂, and carbon dioxide (CO₂) from 1980 to 2019. Climatic conditions, such as warm temperature, high wind speed, dry conditions in the JJA, and SON season, favor the intensity and spread of the fire, which is controlled. The emitted pollutants are transported to neighboring countries and can travel over the Atlantic Ocean, as far as ~6600 km from the source site. Full article

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17 pages, 5227 KiB

Open AccessArticle

Regional Features of Long-Term Exposure to PM_2.5 Air Quality over Asia under SSP Scenarios Based on CMIP6 Models

by Sungbo Shim, Hyunmin Sung, Sanghoon Kwon, Jisun Kim, Jaehee Lee, Minah Sun, Jaeyoung Song, Jongchul Ha, Younghwa Byun, Yeonhee Kim, Steven T. Turnock, David S. Stevenson, Robert J. Allen, Fiona M. O’Connor, Joao C. Teixeira, Jonny Williams, Ben Johnson, James Keeble, Jane Mulcahy and Guang Zeng

Int. J. Environ. Res. Public Health 2021, 18(13), 6817; https://doi.org/10.3390/ijerph18136817 - 25 Jun 2021

Cited by 13 | Viewed by 4449

Abstract

This study investigates changes in fine particulate matter (PM_2.5) concentration and air-quality index (AQI) in Asia using nine different Coupled Model Inter-Comparison Project 6 (CMIP6) climate model ensembles from historical and future scenarios under shared socioeconomic pathways (SSPs). The results indicated [...] Read more.

This study investigates changes in fine particulate matter (PM_2.5) concentration and air-quality index (AQI) in Asia using nine different Coupled Model Inter-Comparison Project 6 (CMIP6) climate model ensembles from historical and future scenarios under shared socioeconomic pathways (SSPs). The results indicated that the estimated present-day PM_2.5 concentrations were comparable to satellite-derived data. Overall, the PM_2.5 concentrations of the analyzed regions exceeded the WHO air-quality guidelines, particularly in East Asia and South Asia. In future SSP scenarios that consider the implementation of significant air-quality controls (SSP1-2.6, SSP5-8.5) and medium air-quality controls (SSP2-4.5), the annual PM_2.5 levels were predicted to substantially reduce (by 46% to around 66% of the present-day levels) in East Asia, resulting in a significant improvement in the AQI values in the mid-future. Conversely, weak air pollution controls considered in the SSP3-7.0 scenario resulted in poor AQI values in China and India. Moreover, a predicted increase in the percentage of aged populations (>65 years) in these regions, coupled with high AQI values, may increase the risk of premature deaths in the future. This study also examined the regional impact of PM_2.5 mitigations on downward shortwave energy and surface air temperature. Our results revealed that, although significant air pollution controls can reduce long-term exposure to PM_2.5, it may also contribute to the warming of near- and mid-future climates. Full article

(This article belongs to the Special Issue Advances in Air Pollution Meteorology Research)

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