Spatial distribution of lichen communities and air pollution mapping in a tropical city: Medellín, Colombia

Introduction: Enough scientific evidence is available on the harmful effects of air pollution on the health of human beings, fauna, flora, and ecosystems in general. The mechanical and electronical monitoring networks are the first option for the air quality diagnosis, but they do not allow a direct and precise assessment of the impacts in living organisms that may result from the exposure to air pollutants. Objective: To evaluate the changes in the composition of corticulous lichen communities as a response to various stress factors in areas with different levels of air quality to diagnose the state of pollution or intervention in an area with a more complete option. Methods: Air quality contrasts and changes in richness and coverage of corticulous lichens in response to different stress factors, such as land use and distance to roads, in three different biomonitoring areas, were evaluate using GIS, and the data are presented in an easy-to-understand grey scale coded isoline map. Results: Indicators such as lichen coverage (R= -0.4) and richness (R= -0.7) are inverse correlated with PM2.5 concentrations in each area. A total of 110 lichen species were identified, being Phaeophyscia chloantha (Ach.) Moberg and Physcia poncinsii Hue the most frequent species (present in 38 and 33 % of the 86 sampled phorophytes, respectively). The intra-area relationships of lichen richness exhibit significant relationships with regards to the land use and distance to roads (with correlations coefficients greater than 0.5) and the Simpson index was higher than 0.9, in places with better conditions in terms of air quality and microenvironments, likewise the resistance factors calculated suggest that the most sensitive species can be found in environments with a lesser degree of disturbance. Conclusion: These evaluations represent more criteria elements for the diagnosis of the environmental health in the biomonitoring areas.

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

10.20944/preprints202006.0158.v1 ◽

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 µg/m3 (IQR: 45.4-73.0 µg/m3; median= 57.5 µg/m3). For the ML LUR models, RMSE values ranged between 5.43 µg/m3 - 15.43 µg/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 µg/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.

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

10.5194/egusphere-egu21-1182 ◽

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

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, &#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 (PM2.5) in Taiwan and to determine key land-use factors controlling various levels of air PM2.5 pollution. First, data on annual air PM2.5 pollution in the Taiwan Island are collected in 2017. Four thresholds of 16.37, 18.68, 21.83, 25.83 &#181;g/m3 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&#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 PM2.5 pollution are determined using stepwise LULR models for various levels of air PM2.5 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.

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

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17145135 ◽

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.

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Ambient air quality in an urban area and its effects on plants and human beings: a case study of Tiruchirappalli, India

Kathmandu University Journal of Science Engineering and Technology ◽

10.3126/kuset.v6i2.4007 ◽

1970 ◽

Vol 6 (2) ◽

pp. 13-19 ◽

Cited By ~ 5

Author(s):

Sirajuddin M Horaginamani ◽

M Ravichandran

Keyword(s):

Air Pollution ◽

Developing Countries ◽

Air Quality ◽

Human Health ◽

Urban Area ◽

Urban Areas ◽

Ambient Air ◽

Control Measures ◽

Human Beings ◽

Ambient Air Quality

Though water and land pollution is very dangerous, air pollution has its own peculiarities, due to its transboundary dispersion of pollutants over the entire world. In any well planned urban set up, industrial pollution takes a back seat and vehicular emissions take precedence as the major cause of urban air pollution. Air pollution is one of the serious problems faced by the people globally, especially in urban areas of developing countries like India. All these in turn lead to an increase in the air pollution levels and have adverse effects on the health of people and plants. Western countries have conducted several studies in this area, but there are only a few studies in developing countries like India. A study on ambient air quality in Tiruchirappalli urban area and its possible effects selected plants and human health has been undertaken, which may be helpful to bring out possible control measures. Keywords: ambient air quality; respiratory disorders; APTI; human health DOI: 10.3126/kuset.v6i2.4007Kathmandu University Journal of Science, Engineering and Technology Vol.6. No II, November, 2010, pp.13-19

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

Advances in Meteorology ◽

10.1155/2016/5494585 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 1

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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A Cluster Analysis of PM2.5 Using CMAQ Model Results for Representativeness of Air Quality Monitoring Networks in Busan, Korea

10.5194/egusphere-egu2020-3189 ◽

2020 ◽

Author(s):

Woo-Sik Jung ◽

Woo-Gon Do

Keyword(s):

Air Pollution ◽

Cluster Analysis ◽

Air Quality ◽

Monitoring Network ◽

Hierarchical Cluster ◽

Quality Monitoring ◽

Quality Data ◽

Air Quality Monitoring ◽

Monitoring Networks ◽

Monitoring Stations

With increasing interest in air pollution, the installation of air quality monitoring networks for regular measurement is considered a very important task in many countries. However, operation of air quality monitoring networks requires much time and money. Therefore, the representativeness of the locations of air quality monitoring networks is an important issue that has been studied by many groups worldwide. Most such studies are based on statistical analysis or the use of geographic information systems (GIS) in existing air quality monitoring network data. These methods are useful for identifying the representativeness of existing measuring networks, but they cannot verify the need to add new monitoring stations. With the development of computer technology, numerical air quality models such as CMAQ have become increasingly important in analyzing and diagnosing air pollution. In this study, PM2.5 distributions in Busan were reproduced with 1-km grid spacing by the CMAQ model. The model results reflected actual PM2.5 changes relatively well. A cluster analysis, which is a statistical method that groups similar objects together, was then applied to the hourly PM2.5 concentration for all grids in the model domain. Similarities and differences between objects can be measured in several ways. K-means clustering uses a non-hierarchical cluster analysis method featuring an advantageously low calculation time for the fast processing of large amounts of data. K-means clustering was highly prevalent in existing studies that grouped air quality data according to the same characteristics. As a result of the cluster analysis, PM2.5 pollution in Busan was successfully divided into groups with the same concentration change characteristics. Finally, the redundancy of the monitoring stations and the need for additional sites were analyzed by comparing the clusters of PM2.5 with the locations of the air quality monitoring networks currently in operation.This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(2017R1D1A3B03036152).

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Spatial Extent and Distribution of Ambient Airborne Particulate Matter (PM2.5) in Selected Land Use Sites in Nairobi, Kenya

Journal of Environmental and Public Health ◽

10.1155/2021/4258816 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Caroline Kiai ◽

Christopher Kanali ◽

Joseph Sang ◽

Michael Gatari

Keyword(s):

Land Use ◽

Air Pollution ◽

Particulate Matter ◽

Air Quality ◽

Low Cost ◽

Airborne Particulate Matter ◽

Spatial Extent ◽

Airborne Particulate ◽

Concentration Levels ◽

Pm2.5 Concentration

Air pollution is one of the most important environmental and public health concerns worldwide. Urban air pollution has been increasing since the industrial revolution due to rapid industrialization, mushrooming of cities, and greater dependence on fossil fuels in urban centers. Particulate matter (PM) is considered to be one of the main aerosol pollutants that causes a significant adverse impact on human health. Low-cost air quality sensors have attracted attention recently to curb the lack of air quality data which is essential in assessing the health impacts of air pollutants and evaluating land use policies. This is mainly due to their lower cost in comparison to the conventional methods. The aim of this study was to assess the spatial extent and distribution of ambient airborne particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) in Nairobi City County. Seven sites were selected for monitoring based on the land use type: high- and low-density residential, industrial, agricultural, commercial, road transport, and forest reserve areas. Calibrated low-cost sensors and cyclone samplers were used to monitor PM2.5 concentration levels and gravimetric measurements for elemental composition of PM2.5, respectively. The sensor percentage accuracy for calibration ranged from 81.47% to 98.60%. The highest 24-hour average concentration of PM2.5 was observed in Viwandani, an industrial area (111.87 μg/m³), and the lowest concentration at Karura (21.25 μg/m³), a forested area. The results showed a daily variation in PM2.5 concentration levels with the peaks occurring in the morning and the evening due to variation in anthropogenic activities and the depth of the atmospheric boundary layer. Therefore, the study suggests that residents in different selected land use sites are exposed to varying levels of PM2.5 pollution on a regular basis, hence increasing the potential of causing long-term health effects.

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EVALUATION AND SUGGESTION OF ENVIRONMENTAL DEGRADATION AND ITS EFFECTS IN AZAMGARH DISTRICT

International Journal of Research -GRANTHAALAYAH ◽

10.29121/granthaalayah.v8.i10.2020.1894 ◽

2020 ◽

Vol 8 (10) ◽

pp. 172-175

Author(s):

Sanjay Kumar Sharma ◽

Raj Narayan Prajapati

Keyword(s):

Climate Change ◽

Water Pollution ◽

Air Pollution ◽

Environmental Degradation ◽

Research Study ◽

Human Beings ◽

Rapid Urbanization ◽

Water Problem ◽

Different Types ◽

Living Organisms

English: In the context of the presented research study area Azamgarh district, the evaluation of environmental degradation and its effects, due to the changing activity of variable development between environment and human beings, the current activities only point towards its insensitivity. Increasing problems around our environment such as soil pollution, water pollution, air pollution and biodiversity havoc, future and future crisis on environment and human existence have arisen resulting in climate change increasing natural disasters, drinking water problem, global warming , Increase in incurable diseases of human beings, there are different types of effects of all living organisms including humans, due to which - environmental degradation and population growth and industrialization, rapid urbanization, consumerist culture have considered the basic root of environmental degradation. The following suggestions will be presented by the researcher environmental impact and evaluation. Hindi: प्रस्तुत शोध अध्ययन क्षेत्र आजमगढ़ जनपद के सन्दर्भ में पर्यावरण अवनयन और उसके प्रभावों का मूल्यांकन पर्यावरण एवं मानव के बीच परिवर्तनशील विकास के बदलते क्रियाकलाप के कारण वर्तमान गतिविधियाँ उसकी असंवेदनशीलता की ओर ही इशारा करती है। हमारे वातावरण के आसपास बढ़ती समस्याएं जैसे मृदा प्रदूषण, जल प्रदूषण, वायु प्रदूषण एवं जैव विविधता का तीव्र ह्नास , पर्यावरण एवं मानव अस्तित्व पर भविष्य के लिए संकट उत्पन्न हो गया जिसके परिणाम स्वरूप जलवायु परिवर्तन प्राकृतिक आपदाओं में वृद्धि, पेयजल की समस्या, वैश्विक उष्मन, मानव के असाध्य रोगों में वृद्धि, मानव सहित सभी जीवधारियों के विभिन्न प्रकार के प्रभाव हैं जिसका कारण-पर्यावरण अवनयन एवं जनसंख्या वृद्धि तथा औद्योगीकरण, तीव्र नगरीकरण, उपभोक्तावादी संस्कृति ने पर्यावरण अवनयन का मूल जड़ माना जा रहा है। शोधार्थी पर्यावरण प्रभाव एवं मूल्यांकन के द्वारा निम्नलिखित सुझाव को प्रस्तुत किया जायेगा।

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Correlation between Air Quality and Wastewater Pollution

10.5772/intechopen.95582 ◽

2021 ◽

Author(s):

Karzan Mohammed Khalid

Keyword(s):

Air Pollution ◽

Air Quality ◽

Urban Areas ◽

Wastewater Treatment Plants ◽

Pollutant Emissions ◽

Negative Effects ◽

Environmental Technology ◽

Wastewater Pollution ◽

Pollutant Sources ◽

Living Organisms

Recently, air pollution is a universal problematic concern which adversely affects global warming and more importantly human body systems. This chapter focuses on the importance of air quality, and indicates the negative effects of emissions originated from both municipal and industrial wastewaters to atmosphere. More importantly, the improvements in wastewater treatment plants to eliminate the crisis of emissions on environment and human health is also clarified. Urbanization and distribution of industrials in urban areas influence the air pollution via releasing pollutants and contaminants to environment. The pollutant emissions from wastewaters are volatile organic compounds, Greenhouse gases and other inorganic pollutants (heavy metals) which are causes to many reactions through atmosphere, then products detriment whole environment and living organisms including human. Moreover, contaminants are also released into air from influents of municipal wastewaters and they are considered as the main resources of most threatened infections in human and other animals. As conclusion, because of the persistently development urbanization and industrialization as the wastewater pollutant sources, the environmental technology regarding wastewater treatments must depend on prevention of emissions to air before thinking on cost and good quality effluents.

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A Parameterized Method for Air-Quality Diagnosis and Its Applications

Advances in Meteorology ◽

10.1155/2012/238589 ◽

2012 ◽

Vol 2012 ◽

pp. 1-10 ◽

Cited By ~ 16

Author(s):

J. Z. Wang ◽

S. L. Gong ◽

X. Y. Zhang ◽

Y. Q. Yang ◽

Q. Hou ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Wind Velocity ◽

Pollution Index ◽

Weather Factors ◽

Opening Ceremony ◽

Higher Temperature ◽

Sport Events ◽

Meteorological Elements ◽

Quality Diagnosis

A parameterized method is developed to diagnose the air quality in Beijing and other cities with an index termed (parameters linking air-quality to meteorological elements PLAM) derived from a correlation between PM10and relevant weather elements based on the data between 2000 and 2007. Key weather factors for diagnosing the air pollution intensity are identified and included in PLAM that include atmospheric condensation of water vapour, wet potential equivalent temperature, and wind velocity. It is found that the poor air quality days with elevated PM10are usually associated with higher PLAM values, featuring higher temperature, humidity, lower wind velocity, and higher stability compared to the averaged values in the same period. Both 24 h and 72 h forecasts provided useful services for the day of the opening ceremony of the Beijing Olympic Games and subsequent sport events. A correlation coefficient of 0.82 was achieved between the forecasts and (air pollution index API) and 0.59 between the forecasts and observed PM10, all reaching the significant level of 0.001, for the summer period. A correction factor was also introduced to enable the PLAM to diagnose the observed PM10concentrations all year round.

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