Differences in magnitude and spatial distribution of urban forest pollution deposition rates, air pollution emissions, and ambient neighborhood air quality in New York City

King, Kristen L.; Johnson, Sarah; Kheirbek, Iyad; Lu, Jacqueline W.T.; Matte, Thomas

doi:10.1016/j.landurbplan.2014.04.009

Cited by 54 publications

(34 citation statements)

References 19 publications

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“…4 Cross-section analysis for modeled PM 2.5 concentrations along the central axis of Beijing contrast in regression mapping results. This is also suggested by King et al (2014). Similarly, the coefficient of mr_1000 (length of major roads within the buffer 1000 m) was smaller in peak hours than in non-peak hours, which was mainly due to a stronger spatial variation in non-peak hours.…”

Section: Spatiotemporal Variations Explained By Lur Modelssupporting

confidence: 62%

“…A similar study (Baumgardner et al 2012) in Mexico City showed that the annual removal rate of peri-urban forests for PM 10 was approximately 2 %. King et al (2014) has also suggested an absence of pollution sources in vegetation-covered areas played a larger role than the effect of pollution removal from vegetation in LUR models. The presence of water body land use variable (wat_500) in many models was largely due to a regional background control site next to Miyun reservoir.…”

Section: Predictor Variablesmentioning

confidence: 99%

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Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

Peng

et al. 2014

Environ Sci Pollut Res

133

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Fine particulate matter (PM 2.5 ) is the major air pollutant in Beijing, posing serious threats to human health. Land use regression (LUR) has been widely used in predicting spatiotemporal variation of ambient air-pollutant concentrations, though restricted to the European and North American context. We aimed to estimate spatiotemporal variations of PM 2.5 by building separate LUR models in Beijing. Hourly routine PM 2.5 measurements were collected at 35 sites from 4th March 2013 to 5th March 2014. Seventy-seven predictor variables were generated in GIS, including street network, land cover, population density, catering services distribution, bus stop density, intersection density, and others. Eight LUR models were developed on annual, seasonal, peak/non-peak, and incremental concentration subsets. The annual mean concentration across all sites is 90.7 μg/m 3 (SD=13.7). PM 2.5 shows more temporal variation than spatial variation, indicating the necessity of building different models to capture spatiotemporal trends. The adjusted R 2 of these models range between 0.43 and 0.65. Most LUR models are driven by significant predictors including major road length, vegetation, and water land use. Annual outdoor exposure in Beijing is as high as 96.5 μg/m 3 . This is among the first LUR studies implemented in a seriously air-polluted Chinese context, which generally produce acceptable results and reliable spatial air-pollution maps. Apart from the models for winter and incremental concentration, LUR models are driven by similar variables, suggesting that the spatial variations of PM 2.5 remain steady for most of the time. Temporal variations are explained by the intercepts, and spatial variations in the measurements determine the strength of variable coefficients in our models.

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Section: Spatiotemporal Variations Explained By Lur Modelssupporting

confidence: 62%

Section: Predictor Variablesmentioning

confidence: 99%

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

Peng

et al. 2014

Environ Sci Pollut Res

133

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“…Also, trees can reduce economic and public health impacts associated with flooding damage and waterborne illnesses (Gaffield et al, 2003). Trees can provide other benefits such as urban heat island reduction, energy savings, carbon storage, and air pollutant removal (Ju and Yoon, 2011; King et al, 2014; Simpson, 1998; Zhao et al, 2010). …”

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confidence: 99%

Surface Water Storage Capacity of Twenty Tree Species in Davis, California

2016

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Urban forestry is an important green infrastructure strategy because healthy trees can intercept rainfall, reducing stormwater runoff and pollutant loading. Surface saturation storage capacity, defined as the thin film of water that must wet tree surfaces before flow begins, is the most important variable influencing rainfall interception processes. Surface storage capacity is known to vary widely among tree species, but it is little studied. This research measured surface storage capacities of 20 urban tree species in a rainfall simulator. The measurement system included a rainfall simulator, digital balance, digital camera, and computer. Eight samples were randomly collected from each tree species. Twelve rainfall intensities (3.5-139.5 mm h) were simulated. Leaf-on and leaf-off simulations were conducted for deciduous species. Stem and foliar surface areas were estimated using an image analysis method. Results indicated that surface storage capacities varied threefold among tree species, 0.59 mm for crape myrtle ( L.) and 1.81 mm for blue spruce ( Engelm.). The mean value across all species was 0.86 mm (0.11 mm SD). To illustrate application of the storage values, interception was simulated and compared across species for a 40-yr period with different rainfall intensities and durations. By quantifying the potential for different tree species to intercept rainfall under a variety of meteorological conditions, this study provides new knowledge that is fundamental to validating the cost-effectiveness of urban forestry as a green infrastructure strategy and designing functional plantings.

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“…Geographic Information System has been used to investigate crime occurrence based on zones of varying distances around each sample (Donovan & Prestemon, ). More researchers are now using light detection and ranging (LiDAR) data sets to distinguish between types of vegetation, including trees, shrubs, and grass (King, Johnson, Kheirbek, Lu, & Matte, ; Larondelle, Hamstead, Kremer, Haase, & McPhearson, ).…”

Section: Introductionmentioning

confidence: 99%

Does vegetation matter for the criminal behavior among people with serious mental illness?

Han

Marzell

Deng

2019

Asian Soc Wrk & Pol Rev

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This study attempts to examine the effect of vegetation on arrests of offenders with and without serious mental illness (SMI). Data from the MacArthur mental health court study were analyzed. Objective arrest data were obtained 12–18 months pre‐ and post‐court involvement between 2005 and 2007. Vegetation data were captured by the normalized difference vegetation index, which can be regarded as a general measure of “vegetation” and linked with GeoID, obtained from the American Community Survey. We analyzed data of sociodemographic factors (e.g., age, gender), crime‐related factors (e.g., age of first arrest), and vegetation in related to the crime on 652 offenders with mental illness. Multilevel negative binomial regression revealed no significant effect of vegetation on arrests among offenders without SMI; however, for those with SMI, a significant variation in vegetation on arrests was found among all study models. Our findings suggest that vegetation is significantly associated with arrests, with a positive effect on reducing the recidivism only among offenders with SMI. Given that practice and policy almost exclusively focus on individual factors of crimes, it is important to have more considerations about how neighborhood factors affect criminal behavior, depending on SMI in order to have more comprehensive methods of crime prevention.

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Differences in magnitude and spatial distribution of urban forest pollution deposition rates, air pollution emissions, and ambient neighborhood air quality in New York City

Cited by 54 publications

References 19 publications

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

Applying land use regression model to estimate spatial variation of PM2.5 in Beijing, China

Surface Water Storage Capacity of Twenty Tree Species in Davis, California

Does vegetation matter for the criminal behavior among people with serious mental illness?

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