scholarly journals The Complexity of Space Utilization and Environmental Pollution Control in the Main Corridor of Makassar City, South Sulawesi, Indonesia

2020 ◽  
Vol 12 (21) ◽  
pp. 9244 ◽  
Author(s):  
Batara Surya ◽  
Hamsina Hamsina ◽  
Ridwan Ridwan ◽  
Baharuddin Baharuddin ◽  
Firman Menne ◽  
...  
Keyword(s):  
Environmental Management ◽  
Land Use ◽  
Air Pollution ◽  
Air Quality ◽  
Environmental Quality ◽  
Pollution Index ◽  
Industrial Area ◽  
Population Mobility ◽  
A Value ◽  
Quality Degradation

Population mobility, increasing demand for transportation, and the complexity of land use have an impact on environmental quality degradation and air quality pollution. This study aims to analyze (1) the effect of population mobility, increased traffic volume, and land use change on air quality pollution, (2) direct and indirect effects of urban activities, transportation systems, and movement patterns on environmental quality degradation and air pollution index, and (3) air pollution strategy and sustainable urban environmental management. The research method used is a sequential explanation design. Data were obtained through observation, surveys, in-depth interviews, and documentation. The results of the study illustrate that the business center and Daya terminal with a value of 0.18 µgram/m3 is polluted, the power plant and Sermani industrial area with a value of 0.16 µgram/m3 is polluted, the Makassar industrial area with a value of 0.23 is heavily polluted, and the Hasanuddin International Airport area with a value of 0.04 µgram/m3 is not polluted. Population mobility, traffic volume, and land use changes have a significant effect on environmental quality degradation, with a determination coefficient of 94.1%. The direct effect of decreasing environmental quality on the air pollution index is 66.09%. This study recommends transportation management on the main road corridor of Makassar City, which is environmentally friendly with regard to sustainable environmental management.

Abstract 36: Mapping and Multilevel Modeling of Climate Change and Air Pollution With Risk of Stroke in the United States and China: Findings From the Drexel-SARI Low Carbon and Healthy City Study

Stroke ◽  
2016 ◽  
Vol 47 (suppl_1) ◽  
Author(s):  
Longjian Liu ◽  
Hui Liu ◽  
Xuan Yang ◽  
Feng Jia ◽  
Mingquan Wang
Keyword(s):  
Climate Change ◽  
Air Pollution ◽  
Air Quality ◽  
Pollution Index ◽  
The United States ◽  
Potential Interaction ◽  
Low Carbon ◽  
Multilevel Regression ◽  
Healthy City ◽  
Nationally Representative

Introduction and Hypothesis: Stroke is a leading cause of death and the major cause of disability in the world. However, few studies applied multilevel regression techniques to explore the association of stroke risk with climate change and air pollution. In the study, we aimed to test the hypothesis that the disproportionately distributed stroke rates across the counties and cities within a country are significantly associated with air pollution and temperature. Methods: We used data from U.S. 1118 counties in 49 states, which had estimated measures of particulate matter (PM)2.5 for the years 2010-2013, and data from China 120 cities in 32 provinces (including 4 municipalities), which had measures of Air Pollution Index (API) for the years 2012-2013. We assessed the association between air quality and prevalence of stroke using spatial mapping, autocorrelation and multilevel regression models. Results: Findings from the U.S. show that the highest average PM2.5 level was in July (10.2 μg/m3) and the lowest in October (7.63 μg/m3) for the years 2010-2013. Annual average PM2.5 levels were significantly different across the 1118 counties, and were significantly associated with stroke rates. Multilevel regression analysis indicated that the prevalence of stroke significantly increased by 1.19% for every 10 μg/m3 increase of PM2.5 (p<0.001). Significant variability in PM2.5 by states was observed (p=0.019). More than 70% of the variation in stroke rates existed across the counties (p=0.017) and 18.7% existed across the states (p=0.047). In China, the highest API was observed in the month of December, with a result of 75.76 in 2012 and 97.51 in 2013. The lowest API was observed in July, with a result of 51.21 in 2012, and 54.23 in 2013. Prevalence of stroke was significantly higher in cities with higher API concentrations. The associations between air quality and risk of stroke were significantly mediated by temperatures. Conclusions: The study, using nationally representative data, is one of the first studies to address a positive and complex association between air quality and prevalence of stroke, and a potential interaction effect of temperatures on the air - stroke association.

scholarly journals Using a Network of Locally Developed Low Cost Particulate Matter Sensors for Land Use Regression Modeling of PM2.5 in Urban Uganda

2020 ◽  
Author(s):  
Eric S. Coker ◽  
Ssematimba Joel ◽  
Engineer Bainomugisha
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Low Cost ◽  
Additive Models ◽  
Pollution Monitoring ◽  
Sub Saharan Africa ◽  
Land Use Regression ◽  
Sub Saharan

Background: There are major air pollution monitoring gaps in sub-Saharan Africa. Developing capacity in the region to conduct air monitoring in the region can help estimate exposure to air pollution for epidemiology research. The purpose of our study is to develop a land use regression (LUR) model using low-cost air quality sensors developed by a research group in Uganda (AirQo). Methods: Using these low-cost sensors, we collected continuous measurements of fine particulate matter (PM2.5) between May 1, 2019 and February 29, 2020 at 22 monitoring sites across urban municipalities of Uganda. We compared average monthly PM2.5 concentrations from the AirQo sensors with measurements from a BAM-1020 reference monitor operated at the US Embassy in Kampala. Monthly PM2.5 concentrations were used for LUR modeling. We used eight Machine Learning (ML) algorithms and ensemble modeling; using 10-fold cross validation and root mean squared error (RMSE) to evaluate model performance. Results: Monthly PM2.5 concentration was 60.2 &micro;g/m3 (IQR: 45.4-73.0 &micro;g/m3; median= 57.5 &micro;g/m3). For the ML LUR models, RMSE values ranged between 5.43 &micro;g/m3 - 15.43 &micro;g/m3 and explained between 28% and 92% of monthly PM2.5 variability. Generalized additive models explained the largest amount of PM2.5 variability (R2=0.92) and produced the lowest RMSE (5.43 &micro;g/m3) in the held-out test set. The most important predictors of monthly PM2.5 concentrations included monthly precipitation, major roadway density, population density, latitude, greenness, and percentage of households using solid fuels. Conclusion: To our knowledge, ours is the first study to model the spatial distribution of urban air pollution in sub-Saharan Africa using air monitors developed from the region itself. Non-parametric ML for LUR modeling performed with high accuracy for prediction of monthly PM2.5 levels. Our analysis suggests that locally produced low-cost air quality sensors can help build capacity to conduct air pollution epidemiology research in the region.

Applying land use logistic regression models to assess different levels of air quality and to determine key environmental factors in Taiwan

2021 ◽  
Author(s):  
Cheng-Shin Jang
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Logistic Regression ◽  
Air Quality ◽  
Geographical Information ◽  
Categorical Variables ◽  
Anthropogenic Factors ◽  
Forest Land ◽  
Use Factors ◽  
Different Levels

&lt;p&gt;Due to fast industrialization and urbanization, air pollution is more and more serious in Taiwan. Generally, many anthropogenic factors can affect air quality; for example, &amp;#160;exhaust gas from automobiles and motorcycles, factory emissions, fossil fuels, burning straw, incinerators, etc. The factors are highly associated with land use. Previous studies typically used multiple linear regression model to analyze the relationships between air quality and land use. This study adopts multi-threshold land use logistic regression (LULR) models with several continuous and categorical variables to assess different levels of fine particulate matters (PM&lt;sub&gt;2.5&lt;/sub&gt;) in Taiwan and to determine key land-use factors controlling various levels of air PM&lt;sub&gt;2.5 &lt;/sub&gt;pollution. First, data on annual air PM&lt;sub&gt;2.5&lt;/sub&gt; pollution in the Taiwan Island are collected in 2017. Four thresholds of 16.37, 18.68, 21.83, 25.83 &amp;#181;g/m&lt;sup&gt;3 &lt;/sup&gt;are determined based on the 20th, 40th, 60th, and 80th percentiles, respectively, of observed data. Geographical information system is then adopted to analyze data on 29 environmental variables obtained from the three main dimensions&amp;#8211;information of land-use categories, amounts of specified pollution sources in townships, and geographical locations adjacent to monitoring stations of air quality. Finally, data in 2017 are employed to establish the LULR model and significant land-use factors causing air PM&lt;sub&gt;2.5&lt;/sub&gt; pollution are determined using stepwise LULR models for various levels of air PM&lt;sub&gt;2.5&lt;/sub&gt; pollution. Moreover, data in 2018 are used to verify the established LULR models. The analyzed results reveal that correct responses of the LULR models range from 83.6% to 100%. For the 20th-percentile threshold, locations and the industry land-use area are positively contributed to air pollution, while tempt densities and building, agriculture, forest land-use areas are negatively contributed to air pollution. For the 40th-percentile threshold, locations, plains with an elevation of less than 150 m, and agriculture land-use areas are related to air pollution. For the 60th-percentile threshold, locations are positively related to air pollution, while forest land-use areas are negatively related to air pollution. For the 80th-percentile threshold, locations and industry park areas associated with air pollution. According to the research results, a feasible strategy of environmental management and outdoor activities is proposed.&lt;/p&gt;

scholarly journals Investigation of the Impact of Land-Use Distribution on PM2.5 in Weifang: Seasonal Variations

2020 ◽  
Vol 17 (14) ◽  
pp. 5135
Author(s):  
Chengming Li ◽  
Kuo Zhang ◽  
Zhaoxin Dai ◽  
Zhaoting Ma ◽  
Xiaoli Liu
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Air Quality ◽  
Nonlinear Effects ◽  
Policy Implications ◽  
Effective Control ◽  
Boosted Regression Tree ◽  
Construction Land ◽  
The Impact ◽  
Effects Of Land Use

As air pollution becomes highly focused in China, the accurate identification of its influencing factors is critical for achieving effective control and targeted environmental governance. Land-use distribution is one of the key factors affecting air quality, and research on the impact of land-use distribution on air pollution has drawn wide attention. However, considerable studies have mostly used linear regression models, which fail to capture the nonlinear effects of land-use distribution on PM2.5 (fine particulate matter with a diameter less than or equal to 2.5 microns) and to show how impacts on PM2.5 vary with land-use magnitudes. In addition, related studies have generally focused on annual analyses, ignoring the seasonal variability of the impact of land-use distribution on PM2.5, thus leading to possible estimation biases for PM2.5. This study was designed to address these issues and assess the impacts of land-use distribution on PM2.5 in Weifang, China. A machine learning statistical model, the boosted regression tree (BRT), was applied to measure nonlinear effects of land-use distribution on PM2.5, capture how land-use magnitude impacts PM2.5 across different seasons, and explore the policy implications for urban planning. The main conclusions are that the air quality will significantly improve with an increase in grassland and forest area, especially below 8% and 20%, respectively. When the distribution of construction land is greater than around 10%, the PM2.5 pollution can be seriously substantially increased with the increment of their areas. The impact of gardens and farmland presents seasonal characteristics. It is noted that as the weather becomes colder, the inhibitory effect of vegetation distribution on the PM2.5 concentration gradually decreases, while the positive impacts of artificial surface distributions, such as construction land and roads, are aggravated because leaves drop off in autumn (September–November) and winter (December–February). According to the findings of this study, it is recommended that Weifang should strengthen pollution control in winter, for instance, expand the coverage areas of evergreen vegetation like Pinus bungeana Zucc. and Euonymus japonicus Thunb, and increase the width and numbers of branches connecting different main roads. The findings also provide quantitative and optimal land-use planning and strategies to minimize PM2.5 pollution, referring to the status of regional urbanization and greening construction.

scholarly journals The Influence of Air Pollution on Corticolous Lichens near the Strathcona Industrial Area, Alberta

2007 ◽  
Vol 121 (1) ◽  
pp. 17 ◽  
Author(s):  
M. Elsinger ◽  
E. Burrell ◽  
N. DeBruyn ◽  
K. Tanasichuk ◽  
K. Timoney
Keyword(s):  
Air Pollution ◽  
Species Richness ◽  
Air Quality ◽  
Species Composition ◽  
Industrial Area ◽  
Pollution Source ◽  
Pollution Gradient ◽  
Mature Trees ◽  
Parmelia Sulcata ◽  
Lichen Community

Lichens that grow on the bark of mature trees were studied at 35 sites along an air pollution gradient east of Edmonton, Alberta. Data on species composition, richness, and cover were recorded in October 1999 in a matrix of sites that extends from a known source of pollutants (the Strathcona Industrial Area) east across Strathcona County. Air pollution is affecting the corticolous lichen community. Lichen species richness and total cover increased with distance from the pollution source. Species richness in areas distant from pollution was roughly twice that in areas near the Strathcona Industrial Area. Xanthoria fallax and Phaeophyscia orbicularis were the most pollution tolerant lichens. Xanthoria hasseana, Ochrolechia arborea, Physcia adscendens, Parmelia sulcata, and Melanelia albertana were rare or absent near the pollution source and common in more distant areas. Most of the 15 species assessed were sensitive to air quality to some degree. Some lichens near the refineries and in Sherwood Park showed abnormal coloration and poor thallus integrity indicative of stress. We discuss implications for human health.

scholarly journals A Simple Tool to Identify Representative Wind Sites for Air Pollution Modelling Applications

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
M. A. Elangasinghe ◽  
K. N. Dirks ◽  
N. Singhal ◽  
J. A. Salmond ◽  
I. Longley ◽  
...  
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Air Quality ◽  
Surface Wind ◽  
Urban Environments ◽  
Wind Data ◽  
Monitoring Site ◽  
Site Characteristics ◽  
A Site ◽  
Air Pollution Model

This paper investigates the use of the Site-Optimized Semiempirical (SOSE) air pollution model to identify the surface wind measurement site characteristics that yield the best air pollution predictions for urban locations. It compares the modelling results from twelve meteorological sites with varying anemometer heights, located at different distances from the air pollution measurements and exhibiting different land use characteristics. The results show that the index of agreement (IA) between observed and predicted concentrations can be improved from 0.4 to 0.8 by using the most compared to the least representative wind data as input to the air pollution model. Although improvements can be achieved using wind data from a site closer to the air quality monitoring site, choosing the closest wind site does not necessarily yield the best results, especially if the meteorological station is located in a region of complex land use. In addition, both the height of the anemometer and the openness of the terrain surrounding the anemometer were found to be equally important in obtaining good model predictions. The simple SOSE model can therefore be used to complement regulatory meteorological guidelines by providing a quantitative assessment of wind site representativeness for air quality applications in complex urban environments.

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