scholarly journals A Multiscale Land Use Regression Approach for Estimating Intraurban Spatial Variability of PM2.5 Concentration by Integrating Multisource Datasets

2021 ◽  
Vol 19 (1) ◽  
pp. 321
Author(s):  
Yuan Shi ◽  
Alexis Kai-Hon Lau ◽  
Edward Ng ◽  
Hung-Chak Ho ◽  
Muhammad Bilal
Keyword(s):  
Land Use ◽  
Air Quality ◽  
Spatial Variability ◽  
Spatial Data ◽  
Meteorological Data ◽  
Quality Data ◽  
Small Scale ◽  
Exposure Level ◽  
Land Use Regression ◽  
Neighborhood Scale

Poor air quality has been a major urban environmental issue in large high-density cities all over the world, and particularly in Asia, where the multiscale complex of pollution dispersal creates a high-level spatial variability of exposure level. Investigating such multiscale complexity and fine-scale spatial variability is challenging. In this study, we aim to tackle the challenge by focusing on PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 µm,) which is one of the most concerning air pollutants. We use the widely adopted land use regression (LUR) modeling technique as the fundamental method to integrate air quality data, satellite data, meteorological data, and spatial data from multiple sources. Unlike most LUR and Aerosol Optical Depth (AOD)-PM2.5 studies, the modeling process was conducted independently at city and neighborhood scales. Correspondingly, predictor variables at the two scales were treated separately. At the city scale, the model developed in the present study obtains better prediction performance in the AOD-PM2.5 relationship when compared with previous studies (R2¯ from 0.72 to 0.80). At the neighborhood scale, point-based building morphological indices and road network centrality metrics were found to be fit-for-purpose indicators of PM2.5 spatial estimation. The resultant PM2.5 map was produced by combining the models from the two scales, which offers a geospatial estimation of small-scale intraurban variability.

Development of Land Use Regression Models Using Regulatory Air Quality Data in Korea

2014 ◽  
Vol 2014 (1) ◽  
pp. 2744 ◽  
Author(s):  
Hwan-Cheol Kim* ◽  
Dal-Young Jung ◽  
Jong-Han Leem ◽  
Sung-Jin Kim
Keyword(s):  
Land Use ◽  
Air Quality ◽  
Regression Models ◽  
Quality Data ◽  
Land Use Regression ◽  
Air Quality Data

scholarly journals GeoMAN: Multi-level Attention Networks for Geo-sensory Time Series Prediction

2018 ◽  
Author(s):  
Yuxuan Liang ◽  
Songyu Ke ◽  
Junbo Zhang ◽  
Xiuwen Yi ◽  
Yu Zheng
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Spatial Data ◽  
Meteorological Data ◽  
External Factors ◽  
Quality Data ◽  
Water Quality Data ◽  
Multi Level ◽  
Spatio Temporal ◽  
Real World Datasets

Numerous sensors have been deployed in different geospatial locations to continuously and cooperatively monitor the surrounding environment, such as the air quality. These sensors generate multiple geo-sensory time series, with spatial correlations between their readings. Forecasting geo-sensory time series is of great importance yet very challenging as it is affected by many complex factors, i.e., dynamic spatio-temporal correlations and external factors. In this paper, we predict the readings of a geo-sensor over several future hours by using a multi-level attention-based recurrent neural network that considers multiple sensors' readings, meteorological data, and spatial data. More specifically, our model consists of two major parts: 1) a multi-level attention mechanism to model the dynamic spatio-temporal dependencies. 2) a general fusion module to incorporate the external factors from different domains. Experiments on two types of real-world datasets, viz., air quality data and water quality data, demonstrate that our method outperforms nine baseline methods.

Application of land use regression to regulatory air quality data in Japan

2009 ◽  
Vol 407 (8) ◽  
pp. 3055-3062 ◽  
Author(s):  
Saori Kashima ◽  
Takashi Yorifuji ◽  
Toshihide Tsuda ◽  
Hiroyuki Doi
Keyword(s):  
Land Use ◽  
Air Quality ◽  
Quality Data ◽  
Land Use Regression ◽  
Air Quality Data

scholarly journals Divergencias y convergencias para asegurar la actividad agrícola en Ecuador: análisis de la parroquia Chuquiribamba (Loja) / Divergences and convergences to ensure agriculture in Ecuador: analysis of the Chuquiribamba Parrish (Loja)

2018 ◽  
Author(s):  
Verónica Iñiguez-Gallardo ◽  
Renato Serrano-Barbecho ◽  
Fabián René Reyes Bueno
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Spatial Data ◽  
Small Scale ◽  
Agricultural Activity ◽  
Spatial Variables ◽  
The People ◽  
Family Economy ◽  
Southern Ecuador ◽  
Parcel Size

La regulación de uso del suelo es un continuo debate en el proceso de planificación territorial, sobre todo en Ecuador, donde la agricultura a pequeña escala es uno de los pilares de la economía familiar para un amplio porcentaje de habitantes del sector rural. Por esta razón, identificar las variables requeridas para mantener la actividad agrícola es una necesidad y obligación. El objetivo principal de este artículo es identificar las variables espaciales que inciden sobre la probabilidad de mantener la actividad agrícola, de acuerdo con las expectativas de la gente y las características del territorio. Para ello, se comparan datos de percepción de los pobladores sobre variables tales como superficie predial, distancia a carretera, a canales de riego y a mercados, con datos espaciales de estas mismas variables. El área de estudio es la Parroquia Chuquiribamba, perteneciente al cantón Loja, al sur del Ecuador, por ser una de las principales fuentes agrícolas del sector. Los resultados sugieren convergencias entre las percepciones de la gente y las variables espaciales necesarias para asegurar la actividad agrícola, así como divergencias respecto a la normativa que regula el tamaño mínimo predial.  Abstract Land-use regulation is an ongoing debate in the process of land-use planning. This is particularly true for a country such as Ecuador, where small-scale agriculture is one of the pillars of the family economy for a large percentage of inhabitants of the rural sector.  In this context, identifying the necessary variables for ensuring agricultural activities is a need and an obligation. The main objective of this article is to identify the spatial variables that affect the probability of maintaining agricultural activity, according to the expectations of the people and the characteristics of the territory. We compare data regarding the perceptions of the people of variables such as parcel size, road, irrigation and market proximity, with spatial data of the same variables. The area of study is the Chuquiribamba Parish, located in Canton Loja, in southern Ecuador. We selected it due to its agricultural importance in the Canton. The results suggest convergences between the perceptions of the people and the spatial variables necessary to safeguard agriculture, as well as divergences with the normative regulating the minimum parcel-size. 

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.

Comparison of Modeled-to-Monitored PM2.5 Exposure Concentrations Resulting from Transportation Emissions in a Near-Road Community

2020 ◽  
Vol 2674 (12) ◽  
pp. 130-143
Author(s):  
Mayra Chavez ◽  
Wen-Whai Li
Keyword(s):  
Air Quality ◽  
Meteorological Data ◽  
Temporal Variations ◽  
Air Pollutant ◽  
Pollutant Emissions ◽  
Spatial And Temporal Variations ◽  
Quality Data ◽  
Background Concentrations ◽  
Regional Background ◽  
Transportation Emissions

Residents living in near-road communities are exposed to traffic-related air pollutants, which can adversely affect their health. Near-road communities are expected to observe significant spatial and temporal variations in pollutant concentrations. Determining these variations in the surrounding areas can help raise awareness among government agencies of these underserved communities living near highways. This study conducted traffic and air quality measurements along with emission and dispersion modeling of the exposure to transportation emissions of a near-road urban community adjacent to the US 54 highway (US 54), with annual average daily traffic (AADT) of 107,237. The objectives of this study were (i) to develop spatial and temporal patterns of pollutant concentration variation and (ii) to apportion the differences in exposure concentrations to background concentrations and those that are contributed from major highways. It was observed that: (a) particulate matter (PM2.5) in near-road communities is dominated by the regional background concentrations which account for more than 85% of the pollution; and (b) only near-road receptors are affected by the traffic-related air pollutant emissions from major highways while spatial and temporal variations of PM2.5 concentrations in near-road communities are less influenced by local traffic, subsiding rapidly to negligible concentrations at 300 m from the road. Modeled PM2.5 concentrations were compared with monitored data. For better air quality impact assessments, higher quality data such as time-specific traffic volume and fleet information as well as site-specific meteorological data could help yield more accurate concentration predictions. Modeled-to-monitored comparison shows that air quality in near-road communities is dominated by regional background concentrations.

Land use regression models to estimate the annual and seasonal spatial variability of sulfur dioxide and particulate matter in Tehran, Iran

2014 ◽  
Vol 488-489 ◽  
pp. 343-353 ◽  
Author(s):  
Hassan Amini ◽  
Seyed Mahmood Taghavi-Shahri ◽  
Sarah B. Henderson ◽  
Kazem Naddafi ◽  
Ramin Nabizadeh ◽  
...  
Keyword(s):  
Land Use ◽  
Particulate Matter ◽  
Sulfur Dioxide ◽  
Spatial Variability ◽  
Regression Models ◽  
Land Use Regression

scholarly journals Land Use and Ambient Air Quality in Bahir Dar and Hawassa, Ethiopia

2018 ◽  
Vol 11 ◽  
pp. 117862211775213 ◽  
Author(s):  
Oluwasinaayomi Faith Kasim ◽  
Muluneh Woldetisadik Abshare ◽  
Truphena Eshibukule Mukuna ◽  
Bolanle Wahab
Keyword(s):  
Land Use ◽  
Air Quality ◽  
Ambient Air ◽  
Urban Land Use ◽  
Quality Data ◽  
Bahir Dar ◽  
Ambient Air Quality ◽  
Industrial Land Use ◽  
Residential Land Use ◽  
Mean Concentration

Land use, air pollution, and climate change are closely related. This article analysed the contributions of urban land use to ambient air quality in Bahir Dar and Hawassa cities. A total of 32 geo-referenced locations, 16 each in Bahir Dar and Hawassa, representing different land uses, were assessed for carbon monoxide (CO), carbon dioxide (CO2), and volatile organic compound (VOC). CO2 concentration (ppm) for Bahir Dar and Hawassa ranged from 385.10 ± 15.34 ppm (recreational land use) to 555.50 ± 80.79 ppm (commercial land use) and 388.07 ± 19.79 ppm (recreational land use) to 444.50 ± 54.05 ppm (industrial land use), respectively, whereas mean concentration of CO was 0.01 ± 0.01 ppm (recreational land use) to 2.59 ± 0.69 ppm (circulation land use) and 0.12 ± 0.11 ppm (recreational land use) to 4.66 ± 1.41 ppm (circulation land use), respectively. The VOC values were 882.10 ± 147.05 ppm (residential land use) to 1436.00 ± 932.06 ppm (institutional land use) and 1377.30 ± 233.23 ppm (institutional land use) to 2132.33 ± 739.71 ppm (circulation land use). Inadequate monitoring, occasioned by dearth of equipment, poor urban management strategy, fossil fuel combustion, and aged vehicles were some of the factors responsible for the observed concentrations. Elevated levels of CO, CO2, and VOC in the atmosphere have a significant impact on global warming, with adverse effects on human health. Capacity for monitoring, analysis, reporting, and validation of air quality data in the cities should be strengthened.

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