Combined land-use and street view image model for estimating black carbon concentrations in urban areas

Land use changes have significantly altered the natural environment in which humans live. In urban areas, diminishing air quality poses a large threat to human health. In order to investigate the relationship between land use/cover change (LUCC) and air pollutants of Wuyishan City between 2014–2017, an integrated approach was used by combining remote sensing techniques with a landscape ecology methods. Annual, seasonal, and weekly mean values of air pollutant (SO2, NO2, CO, PM10, O3, PM2.5, black carbon) concentration and atmospheric visibility were calculated to develop a Pearson correlation between LUCC and air pollutants concentration. Results showed an increase in forested areas (1.79%) and water areas (15.89%), with a simultaneous reduction in cultivated land (6.47%), bare land (72.61%), and built-up land (16.03%) from 2014 to 2017. The transition matrix of land use types revealed that (i) forest expansion took place mainly at the expense of cultivated land (13.94%) and bare land (27.48%); and (ii) water area expansion took place mainly at the expense of cultivated land (1.29%) and forests (0.21%). In 2017, the proportion of days with AQI level I (94.52%) was higher than that in 2014 (88.77%). Additionally, the annual average visibility in 2017 (37.42 km) was higher than 2014 (27.46 km). The concentration of SO2, CO, O3, and black carbon was positively correlated with the cultivated land. The concentration of SO2, CO, and black carbon negatively correlated with the increase of forests. PM10, and PM2.5 is negatively correlated with the water area. Visibility was found to be positively correlated with forested area, and negatively correlated with cultivated land. The findings from this study represent a valuable gain in understanding of policies aimed at improving, safeguarding, and monitoring air quality. These results can be used to inform land-use planning decisions in a comprehensive way and could be a valuable tool for LUCC rational management strategies.

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Using Mobile Monitoring to Develop Hourly Land Use Regression Models of Particle Number and Black Carbon Concentrations

ISEE Conference Abstracts ◽

10.1289/isee.2017.2017-551 ◽

2018 ◽

Vol 2017 (1) ◽

pp. 551

Author(s):

Steve Hankey ◽

Peter Sforza ◽

Brian Farrell ◽

Matt Pierson

Keyword(s):

Land Use ◽

Black Carbon ◽

Regression Models ◽

Particle Number ◽

Mobile Monitoring ◽

Land Use Regression ◽

Carbon Concentrations

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IMPACTS OF URBAN LAND USE ON CRIME PATTERNS THROUGH GIS APPLICATION

PLANNING MALAYSIA JOURNAL ◽

10.21837/pmjournal.v11.i2.113 ◽

2013 ◽

Vol 11 (2) ◽

Cited By ~ 1

Author(s):

Ahmad Nazri Muhamad Ludin ◽

Norsiah Abd. Aziz ◽

Nooraini Hj Yusoff ◽

Wan Juliyana Wan Abd Razak

Keyword(s):

Land Use ◽

Drug Abuse ◽

Land Use Planning ◽

Urban Areas ◽

Spatial Relationship ◽

Police Department ◽

Strong Relationship ◽

Urban Land ◽

Urban Land Use ◽

Land Use Patterns

Land use planning plays a crucial role in creating a balance between the needs of society, physical development and the ecosystem. However, most often poor planning and displacement of land uses particularly in urban areas contribute to social ills such as drug abuse and criminal activities. This research explains the spatial relationship of drug abuse and other criminal activities on urban land use planning and their implications on the society at large. Spatial statistics was used to show patterns, trends and spatial relationships of crimes and land use planning. Data on crime incidents were obtained from the Royal Malaysia Police Department whilst cases of drug abuse were collected from the National Anti-Drug Agency (AADK). Analysis of the data together with digital land use maps produced by Arnpang Jaya Municipal Council, showed the distribution of crime incidents and drug abuse in the area. Findings of the study also indicated that, there was a strong relationship between petty crimes, drng abuse and land use patterns. These criminal activities tend to concentrate in residential and commercial areas of the study area.

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EFFECTS OF LAND USE POLICIES ON KARST DISTURBANCE IN URBAN AREAS

10.1130/abs/2019am-337844 ◽

2019 ◽

Author(s):

Brittiny Paige Moore ◽

Keyword(s):

Land Use ◽

Urban Areas ◽

Land Use Policies ◽

Effects Of Land Use

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Spatial-Temporal Land Use and Land Cover Changes in Urban Areas Using Remote Sensing Images and GIS Analysis: The Case Study of Opole, Poland

Geosciences ◽

10.3390/geosciences11080312 ◽

2021 ◽

Vol 11 (8) ◽

pp. 312

Author(s):

Barbara Wiatkowska ◽

Janusz Słodczyk ◽

Aleksandra Stokowska

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Satellite Imagery ◽

Urban Areas ◽

Agricultural Land ◽

Urban Expansion ◽

Landsat 8 ◽

Rate Of Increase ◽

Management Procedures

Urban expansion is a dynamic and complex phenomenon, often involving adverse changes in land use and land cover (LULC). This paper uses satellite imagery from Landsat-5 TM, Landsat-8 OLI, Sentinel-2 MSI, and GIS technology to analyse LULC changes in 2000, 2005, 2010, 2015, and 2020. The research was carried out in Opole, the capital of the Opole Agglomeration (south-western Poland). Maps produced from supervised spectral classification of remote sensing data revealed that in 20 years, built-up areas have increased about 40%, mainly at the expense of agricultural land. Detection of changes in the spatial pattern of LULC showed that the highest average rate of increase in built-up areas occurred in the zone 3–6 km (11.7%) and above 6 km (10.4%) from the centre of Opole. The analysis of the increase of built-up land in relation to the decreasing population (SDG 11.3.1) has confirmed the ongoing process of demographic suburbanisation. The paper shows that satellite imagery and GIS can be a valuable tool for local authorities and planners to monitor the scale of urbanisation processes for the purpose of adapting space management procedures to the changing environment.

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Correlating elements content in mosses collected in 2015 across Germany with spatially associated characteristics of sampling sites and their surroundings

Environmental Sciences Europe ◽

10.1186/s12302-019-0260-7 ◽

2019 ◽

Vol 31 (1) ◽

Author(s):

Stefan Nickel ◽

Winfried Schröder

Keyword(s):

Land Use ◽

Population Density ◽

Atmospheric Deposition ◽

Vegetation Structure ◽

Urban Areas ◽

Explanatory Power ◽

Spatial Density ◽

Area Index ◽

Explanatory Variables ◽

Tree Layer

Abstract Background The aim of the study was a statistical evaluation of the statistical relevance of potentially explanatory variables (atmospheric deposition, meteorology, geology, soil, topography, sampling, vegetation structure, land-use density, population density, potential emission sources) correlated with the content of 12 heavy metals and nitrogen in mosses collected from 400 sites across Germany in 2015. Beyond correlation analysis, regression analysis was performed using two methods: random forest regression and multiple linear regression in connection with commonality analysis. Results The strongest predictor for the content of Cd, Cu, Ni, Pb, Zn and N in mosses was the sampled species. In 2015, the atmospheric deposition showed a lower predictive power compared to earlier campaigns. The mean precipitation (2013–2015) is a significant factor influencing the content of Cd, Pb and Zn in moss samples. Altitude (Cu, Hg and Ni) and slope (Cd) are the strongest topographical predictors. With regard to 14 vegetation structure measures studied, the distance to adjacent tree stands is the strongest predictor (Cd, Cu, Hg, Zn, N), followed by the tree layer height (Cd, Hg, Pb, N), the leaf area index (Cd, N, Zn), and finally the coverage of the tree layer (Ni, Cd, Hg). For forests, the spatial density in radii 100–300 km predominates as significant predictors for Cu, Hg, Ni and N. For the urban areas, there are element-specific different radii between 25 and 300 km (Cd, Cu, Ni, Pb, N) and for agricultural areas usually radii between 50 and 300 km, in which the respective land use is correlated with the element contents. The population density in the 50 and 100 km radius is a variable with high explanatory power for all elements except Hg and N. Conclusions For Europe-wide analyses, the population density and the proportion of different land-use classes up to 300 km around the moss sampling sites are recommended.

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Development of Land Use Regression Model for Seasonal Variation of Nitrogen Dioxide (NO2) in Lahore, Pakistan

Sustainability ◽

10.3390/su13094933 ◽

2021 ◽

Vol 13 (9) ◽

pp. 4933

Author(s):

Saimar Pervez ◽

Ryuta Maruyama ◽

Ayesha Riaz ◽

Satoshi Nakai

Keyword(s):

Land Use ◽

Air Pollution ◽

Regression Model ◽

Urban Areas ◽

Industrial Development ◽

Ambient Air ◽

Ambient Air Pollution ◽

Land Use Classification ◽

Land Use Regression ◽

Air Exposure

Ambient air pollution and its exposure has been a worldwide issue and can increase the possibility of health risks especially in urban areas of developing countries having the mixture of different air pollution sources. With the increase in population, industrial development and economic prosperity, air pollution is one of the biggest concerns in Pakistan after the occurrence of recent smog episodes. The purpose of this study was to develop a land use regression (LUR) model to provide a better understanding of air exposure and to depict the spatial patterns of air pollutants within the city. Land use regression model was developed for Lahore city, Pakistan using the average seasonal concentration of NO2 and considering 22 potential predictor variables including road network, land use classification and local specific variable. Adjusted explained variance of the LUR models was highest for post-monsoon (77%), followed by monsoon (71%) and was lowest for pre-monsoon (70%). This is the first study conducted in Pakistan to explore the applicability of LUR model and hence will offer the application in other cities. The results of this study would also provide help in promoting epidemiological research in future.

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Study of Temporal Variations of Equivalent Black Carbon in a Coastal City in Northwest Spain Using an Atmospheric Aerosol Data Management Software

Applied Sciences ◽

10.3390/app11020516 ◽

2021 ◽

Vol 11 (2) ◽

pp. 516

Author(s):

María Piñeiro-Iglesias ◽

Javier Andrade-Garda ◽

Sonia Suárez-Garaboa ◽

Soledad Muniategui-Lorenzo ◽

Purificación López-Mahía ◽

...

Keyword(s):

Data Management ◽

Black Carbon ◽

Light Absorption ◽

Atmospheric Aerosol ◽

Urban Areas ◽

Radiative Properties ◽

Anthropogenic Sources ◽

Biomass Combustion ◽

Management Support ◽

Diurnal Variability

Light-absorbing carbonaceous aerosols (including black carbon (BC)) pose serious health issues and play significant roles in atmospheric radiative properties. Two-year measurements (2015–2016) of aerosol light absorption, combined with measurements of sub-micrometric particles, were continuously conducted in A Coruña (northwest (NW) Spain) to determine their light absorption properties: absorption coefficients (σabs) and the absorption Ångström exponent (AAE). The mean and standard deviation of equivalent black carbon (eBC) during the period of study were 0.85 ± 0.83 µg m−3, which are lower than other values measured in urban areas of Spain and Europe. High eBC concentrations found in winter are associated with an increase in emissions from anthropogenic sources in combination with lower mixing layer heights and frequent stagnant conditions. The pronounced diurnal variability suggests a strong influence from local sources. AAE had an average value of 1.26 ± 0.22 which implies that both fossil fuel combustion and biomass burning influenced optical aerosol properties. This also highlights biomass combustion in suburban areas, where the use of wood for domestic heating is encouraged, as an important source of eBC. All data treatment was gathered using SCALA© as atmospheric aerosol data management support software program.

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