scholarly journals Medellin Air Quality Initiative (MAUI)

2021 ◽  
Author(s):  
Andres Yarce Botero ◽  
Olga Lucia Quintero Montoya ◽  
Santiago Lopez-Restrepo ◽  
Nicolás Pinel ◽  
Jhon Edinson Hinestroza ◽  
...  
Keyword(s):  
Land Use ◽  
Air Quality ◽  
Human Health ◽  
Long Range ◽  
Low Cost ◽  
Region Of Interest ◽  
Pollution Assessment ◽  
Health Impact ◽  
Cellular Damage

This chapter book presents Medellín Air qUality Initiative or MAUI Project; it tells a brief story of this teamwork, their scientific and technological directions. The modeling work focuses on the ecosystems and human health impact due to the exposition of several pollutants transported from long-range places and deposited. For this objective, the WRF and LOTOS-EUROS were configurated and implemented over the región of interest previously updating some input conditions like land use and orography. By other side, a spinoff initiative named SimpleSpace was also born during this time, developing, through this instrumentation branch a very compact and modular low-cost sensor to deploy in new air quality networks over the study domain. For testing this instrument and find an alternative way to measure pollutants in the vertical layers, the Helicopter In-Situ Pollution Assessment Experiment HIPAE misión was developed to take data through the overflight of a helicopter over Medellín. From the data obtained from the Simple units and other experiments in the payload, a citogenotoxicity analysis quantify the cellular damage caused by the exposition of the pollutants.

scholarly journals Crowdsourcing LUCAS: Citizens Generating Reference Land Cover and Land Use Data with a Mobile App

2020 ◽  
Author(s):  
Juan Carlos Laso Bayas ◽  
Linda See ◽  
Hedwig Bartl ◽  
Tobias Sturn ◽  
Mathias Karner ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Survey Data ◽  
Target Location ◽  
Low Cost ◽  
Mobile App ◽  
Sample Survey ◽  
In Situ Data

There are many new land use and land cover (LULC) products emerging yet there is still a lack of in-situ data for training, validation, and change detection purposes. The LUCAS (Land Use Cover Area frame Sample) survey is one of the few authoritative in-situ field campaigns, which takes place every three years in European Union member countries. More recently, a study has considered whether citizen science and crowdsourcing could complement LUCAS survey data, e.g., through the FotoQuest Austria mobile app and crowdsourcing campaign. Although the data obtained from the campaign were promising when compared with authoritative LUCAS survey data, there were classes that were not well classified by the citizens, and the photographs submitted through the app were not always of sufficient quality. For this reason, in the latest FotoQuest Go Europe 2018 campaign, several improvements were made to the app to facilitate interaction with the citizens contributing and to improve their accuracy in LULC identification. In addition to extending the locations from Austria to Europe, a change detection component (comparing land cover in 2018 to the 2015 LUCAS photographs) was added, as well as an improved LC decision tree and a near real-time quality assurance system to provide feedback on the distance to the target location, the LULC classes chosen and the quality of the photographs. Another modification was the implementation of a monetary incentive scheme in which users received between 1 to 3 Euros for each successfully completed quest of sufficient quality. The purpose of this paper is to present these new features and to compare the results obtained by the citizens with authoritative LUCAS data from 2018 in terms of LULC and change in LC. We also compared the results between the FotoQuest campaigns in 2015 and 2018 and found a significant improvement in 2018, i.e., a much higher match of LC between FotoQuest Go Europe and LUCAS. Finally, we present the results from a user survey to discuss challenges encountered during the campaign and what further improvements could be made in the future, including better in-app navigation and offline maps, making FotoQuest a model for enabling the collection of large amounts of land cover data at a low cost.

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 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.

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

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.

scholarly journals Response of dust emissions in southwestern North America to 21st century trends in climate, CO<sub>2</sub> fertilization, and land use: implications for air quality

2021 ◽  
Vol 21 (1) ◽  
pp. 57-68
Author(s):  
Yang Li ◽  
Loretta J. Mickley ◽  
Jed O. Kaplan
Keyword(s):  
Land Use ◽  
New Mexico ◽  
Air Quality ◽  
North America ◽  
Human Health ◽  
21St Century ◽  
Fine Dust ◽  
Co2 Fertilization ◽  
Dust Emissions ◽  
Dust Mobilization

Abstract. Climate models predict a shift toward warmer and drier environments in southwestern North America. The consequences of such a shift for dust mobilization and dust concentration are unknown, but they could have large implications for human health, given the connections between dust inhalation and disease. Here we link a dynamic vegetation model (LPJ-LMfire) to a chemical transport model (GEOS-Chem) to assess the impacts of future changes in three factors – climate, CO2 fertilization, and land use practices – on vegetation in this region. From there, we investigate the impacts of changing vegetation on dust mobilization and assess the net effect on fine dust concentration (defined as dust particles less than 2.5 µm in diameter) on surface air quality. We find that surface temperatures in southwestern North America warm by 3.3 K and precipitation decreases by nearly 40 % by 2100 in the most extreme warming scenario (RCP8.5; RCP refers to Representative Concentration Pathway) in spring (March, April, and May) – the season of greatest dust emissions. Such conditions reveal an increased vulnerability to drought and vegetation die-off. Enhanced CO2 fertilization, however, offsets the modeled effects of warming temperatures and rainfall deficit on vegetation in some areas of the southwestern US. Considering all three factors in the RCP8.5 scenario, dust concentrations decrease over Arizona and New Mexico in spring by the late 21st century due to greater CO2 fertilization and a more densely vegetated environment, which inhibits dust mobilization. Along Mexico's northern border, dust concentrations increase as a result of the intensification of anthropogenic land use. In contrast, when CO2 fertilization is not considered in the RCP8.5 scenario, vegetation cover declines significantly across most of the domain by 2100, leading to widespread increases in fine dust concentrations, especially in southeastern New Mexico (up to ∼ 2.0 µg m−3 relative to the present day) and along the border between New Mexico and Mexico (up to ∼ 2.5 µg m−3). Our results have implications for human health, especially for the health of the indigenous people who make up a large percentage of the population in this region.

Does an Increase in Air Quality Models’ Resolution Bring Surface Ozone Concentrations Closer to Reality?

2008 ◽  
Vol 25 (11) ◽  
pp. 1955-1968 ◽  
Author(s):  
Myrto Valari ◽  
Laurent Menut
Keyword(s):  
Land Use ◽  
Air Quality ◽  
Spatial Scales ◽  
Surface Ozone ◽  
Health Impact ◽  
Small Scale ◽  
Population Exposure ◽  
Emission Data ◽  
Mean Values ◽  
Deposition Processes

Abstract A persistent challenge for small-scale air quality modeling is the assessment of health impact and population exposure studies. Despite progress in computation and in the quality of model input (i.e., high-resolution information on land use and emission patterns), the uncertainty associated with input parameters cannot be eliminated. The aim of this paper is to study different sources of uncertainty that affect model results as the resolution increases. Mesoscale chemistry transport simulations at different resolutions are used and modeled 03 concentrations are compared with surface measurements. The case study consists of CHIMERE model simulations over the city of Paris. It is shown that the principal source of noise in model results is the resolution of the input emission fluxes. The O3 concentrations modeled with simulations forced by several horizontal resolutions of input emission data (from Δx = 48 km to Δx = 6 km) indicate that model results do not improve monotonously with resolution, but that after a certain point discrepancies become larger. Based on this result and as an alternative to the deterministic downscaling that resolves explicitly the finer scale (beyond the 1-km range), the authors propose a subgrid-scale approach that uses a statistical description of spatial scales finer than model resolution. As an example, the subgrid variability of modeled O3 concentration has been quantified, when modeled dry deposition processes occur over subgrid surfaces (land use fractions). The implementation of this modified calculation gives access to subgrid fluxes and subgrid surface concentrations instead of the mean values provided by the commonly used model calculation.

scholarly journals The Impacts of Prescribed Fire on PM2.5 Air Quality and Human Health: Application to Asthma-Related Emergency Room Visits in Georgia, USA

2019 ◽  
Vol 16 (13) ◽  
pp. 2312 ◽  
Author(s):  
Ran Huang ◽  
Yongtao Hu ◽  
Armistead G. Russell ◽  
James A. Mulholland ◽  
M. Talat Odman
Keyword(s):  
Air Quality ◽  
Emergency Room ◽  
Human Health ◽  
Health Effects ◽  
Prescribed Burning ◽  
Health Impact ◽  
Temporal Variations ◽  
Prescribed Burn ◽  
Short Term Exposure ◽  
The Impact

Short-term exposure to fire smoke, especially particulate matter with an aerodynamic diameter less than 2.5 µm (PM2.5), is associated with adverse health effects. In order to quantify the impact of prescribed burning on human health, a general health impact function was used with exposure fields of PM2.5 from prescribed burning in Georgia, USA, during the burn seasons of 2015 to 2018, generated using a data fusion method. A method was developed to identify the days and areas when and where the prescribed burning had a major impact on local air quality to explore the relationship between prescribed burning and acute health effects. The results showed strong spatial and temporal variations in prescribed burning impacts. April 2018 exhibited a larger estimated daily health impact with more burned areas compared to Aprils in previous years, likely due to an extended burn season resulting from the need to burn more areas in Georgia. There were an estimated 145 emergency room (ER) visits in Georgia for asthma due to prescribed burning impacts in 2015 during the burn season, and this number increased by about 18% in 2018. Although southwestern, central, and east-central Georgia had large fire impacts on air quality, the absolute number of estimated ER asthma visits resulting from burn impacts was small in these regions compared to metropolitan areas where the population density is higher. Metro-Atlanta had the largest estimated prescribed burn-related asthma ER visits in Georgia, with an average of about 66 during the reporting years.

Sign in / Sign up

Export Citation Format

Share Document