Investigating the effects of the greenery increase on air temperature, ventilation and cooling energy demand in Melbourne with the Weather Research and Forecasting model and Local Climate Zones.

Vegetation has a well-known potential for mitigating urban overheating. This work aims to explore the effects of enhancing urban greenery in Melbourne (Australia) through a configuration of the Weather Research and Forecasting (WRF) model including the Building Effect Parameterization and the Local Climate Zones and presents novelties in: i) covering two-months and ii) focusing on air circulation and buildings cooling energy demand through the ventilation coefficient (VC) and the cooling degree hours (CDHs). A control case and two "what-if" scenarios with a growing green coverage equal to 35 % (control case), 50 % (modest increase) and 60 % (robust increase) have been designed and then simulated for January and February 2019. Outcomes reveal a maximum drop in 2 m temperature of approximately 0.4 °C and 0.8 °C at 14:00 LT for the modest and robust green increase scenario, respectively. The urban-rural energy surplus for cooling buildings is reduced and even counterbalanced. Peak CDHs decrease from 143 °C·h of the control case to 135 °C·h (modest increase) and 126 °C·h (robust increase), while they measure 137 °C·h in the non-urban areas. Average wind speed increases by 0.8 m/s (equal to 22 % with respect to the control case). Furthermore, adding urban greenery has an unfavorable implication on VC (maximum reduction of 500 m2s-1) with a consequent deterioration of the transport and dispersion of pollutants. Middle- and high-density classes are touched more than low-density by the VC reduction. In addition, the benefits of enhancing urban greenery concern physiologically and psychologically the quality of life of the dwellers.

Estimation of plant pollution removal capacity based on intensive air quality measurements.

This study investigated the role of present vegetation in improving air quality in Bucharest (Romania) by analyzing six years of air quality data (PM10 and NO2) from multiple monitoring stations. The target value for human health protection is regularly exceeded for PM10 and not for NO2 over time. Road traffic has substantially contributed (over 70%) to ambient PM10 and NO2 levels. The results showed high seasonal variations in pollutant concentrations, with a pronounced effect of vegetation in reducing PM10 and NO2 levels. Indeed, air quality improvements of 7% for PM10 and 25% for NO2 during the growing season were reported. By using Principal Component Analysis and pollution data subtraction methodology, we have disentangled the impact of vegetation on air pollution and observed distinct annual patterns, particularly higher differences in PM10 and NO2 concentrations during the warm season. Despite limitations such as a lack of full tree inventory for Bucharest and a limited number of monitoring stations, the study highlighted the efficiency of urban vegetation to mitigate air pollution.

A comprehensive review of urban vegetation as a Nature-based Solution for sustainable management of particulate matter in ambient air.

Air pollution is one of the most pressing environmental threats worldwide, resulting in several health issues such as cardiovascular and respiratory disorders, as well as premature mortality. The harmful effects of air pollution are particularly concerning in urban areas, where mismanaged anthropogenic activities, such as growth in the global population, increase in the number of vehicles, and industrial activities, have led to an increase in the concentration of pollutants in the ambient air. Among air pollutants, particulate matter is responsible for most adverse impacts. Several techniques have been implemented to reduce particulate matter concentrations in the ambient air. However, despite all the threats and awareness, efforts to improve air quality remain inadequate. In recent years, urban vegetation has emerged as an efficient Nature-based Solution for managing environmental air pollution due to its ability to filter air, thereby reducing the atmospheric concentrations of particulate matter. This review characterizes the various mitigation mechanisms for particulate matter by urban vegetation (deposition, dispersion, and modification) and identifies key areas for further improvements within each mechanism. Through a systematic assessment of existing literature, this review also highlights the existing gaps in the present literature that need to be addressed to maximize the utility of urban vegetation in reducing particulate matter levels. In conclusion, the review emphasizes the urgent need for proper air pollution management through urban vegetation by integrating different fields, multiple stakeholders, and policymakers to support better implementation.

Spatiotemporal variation in sensitivity of urban vegetation growth and greenness to vegetation water content: Evidence from Chinese megacities.

Understanding the sensitivity of vegetation growth and greenness to vegetation water content change is crucial for elucidating the mechanism of terrestrial ecosystems response to water availability change caused by climate change. Nevertheless, we still have limited knowledge of such aspects in urban in different climatic contexts under the influence of human activities. In this study, we employed Google Earth Engine (GEE), remote sensing satellite imagery, meteorological data, and Vegetation Photosynthesis Model (VPM) to explore the spatiotemporal pattern of vegetation growth and greenness sensitivity to vegetation water content in three megacities (Beijing, Shanghai, and Guangzhou) located in eastern China from 2001 to 2020. We found a significant increase (slope > 0, p < 0.05) in the sensitivity of urban vegetation growth and greenness to vegetation water content (SLSWI). This indicates the increasing dependence of urban vegetation ecosystems on vegetation water resources. Moreover, evident spatial heterogeneity was observed in both SLSWI and the trends of SLSWI, and spatial heterogeneity in SLSWI and the trends of SLSWI was also present among identical vegetation types within the same city. Additionally, both SLSWI of vegetation growth and greenness and the trend of SLSWI showed obvious spatial distribution differences (e.g., standard deviations of trends in SLSWI of open evergreen needle-leaved forest of GPP is 14.36 × 10-2 and standard deviations of trends in SLSWI of open evergreen needle-leaved forest of EVI is 10.16 × 10-2), closely associated with factors such as vegetation type, climatic conditions, and anthropogenic influences.

Rapid urbanization and meteorological changes are reshaping the urban vegetation pattern in urban core area: A national 315-city study in China.

Urban vegetation takes on the responsibility of improving the urban environment and human wellbeing. However, the changing pattern and its driving mechanism are still not well understood at the national scale, especially in China under nearly 20 years-long rapid urbanization. In this study, for urban core area in 315 cities, over 18,000 high-resolution remote sensing images across 18 years were used to detect the spatiotemporal changes of urban vegetation and furtherly explore the interaction and independence of rapid urbanization and meteorological change. We found that, urban vegetation coverage decreased from 12.23 % to 5.91 % (-0.35 % per year) in 2003 to 2020. Urban vegetation per capita presented a steeper decline by 68 % (-0.51 m2 per capita per year) from 18.94 m2 in 2003 to 9.83 m2 in 2020. Spatially, the northwest and central-south zone decreased faster at the regional scale, and small cities contribute the higher decreasing rate. From 2003 to 2020, urbanization is the significant negative factor which contribute to 29.6 % of the reduction, and the meteorological factors do not affect urban vegetation change. Also, we found that the temporal pattern of urban vegetation change could be separated into two stages, including a rapid decline stage (2009-2020) and a progressively declining stage (2003-2008), each has its own driving mechanism. From 2003 to 2008, the decline in urban vegetation had insignificant relationship with meteorological changes and rapid urbanization. However, from 2009 to 2020, urbanization became the most critical factor to affect the urban vegetation, the contribution of urbanization rises to 30.3 %, meteorological factors contribute 14.3 % to the variation (r2 = 0.52). A growing crisis awareness of the rapid decline (especially in 2009 to 2020) of urban vegetation should return to the public scene, and these findings may provide some essential suggestions for securing this urban ecological barrier.

Magnetic and elemental characterization of the particulate matter deposited on leaves of urban trees in Santiago, Chile.

Airborne particulate matter is a serious threat to human health, especially in fast-growing cities. In this study, we carried out a magnetic and elemental study on tree leaves used as passive captors and urban dust from various sites in the city of Santiago, Chile, to assess the reliability of magnetic and elemental measurements to characterize particulate matter pollution from vehicular origin. We found that the magnetic susceptibility and saturation isothermal remanent magnetization measured on urban tree leaves is a good proxy for tracing anthropogenic metallic particles and allow controlling the exposure time for particulate matter collection, in agreement with other studies carried out in large cities. Similar measurements on urban soil can be influenced by particles of detritic (natural) origin, and therefore, magnetic measurements on tree leaves can help to identify hotspots where fine particles are more abundant. Elemental particle-induced X-ray emission analysis of tree leaves showed the presence of a number of elements associated with vehicular emissions, in particular Cu, Zn, Fe, K and S which are present at every site, and As, Se, V, Ni, Sr, Zr, Mo and Pb identified at some sites. We observed a correlation between magnetic parameters and the concentrations of S and Br as well as Cu to a smaller extent. Moreover, this study shows the importance of selecting carefully the tree species as well as the location of trees in order to optimize phytoremediation.

The influence of urban trees and total vegetation on asthma development in children.

Objective: We aimed to assess whether the influence of urban vegetation on asthma development in children (<13 years) varies by type (e.g., total vegetation, tree type, and grass) and season. Methods: We used a cohort of all children born in Montreal, Canada, between 2000 and 2015. Children and cases were identified from linked medico-administrative databases. Exposure to residential vegetation was estimated using the Normalized Difference Vegetation Index (NDVI) for total vegetation and using the total area covered by deciduous and evergreen crowns for trees in 250 m buffers centered on residential postal codes. Seasonal variations in vegetation were modeled by setting values to zero on days outside of pollen and leaf-on seasons. Cox models with vegetation exposures, age as a time axis, and adjusted for sex, material deprivation, and health region were used to estimate hazard ratios (HR) for asthma development. Results: We followed 352,946 children for a total of 1,732,064 person-years and identified 30,816 incident cases of asthma. While annual vegetation (total and trees) measures did not appear to be associated with asthma development, models for pollen and leaf-on seasons yielded significant nonlinear associations. The risk of developing asthma was lower in children exposed to high levels (>33,300 m2) of deciduous crown area for the leaf-on season (HR = 0.69; 95% confidence interval [CI] = 0.67, 0.72) and increased for the pollen season (HR = 1.07; 95% CI =1.02, 1.12), compared with unexposed children. Similar results were found with the Normalized Difference Vegetation Index. Conclusion: The relationship between urban vegetation and childhood asthma development is nonlinear and influenced by vegetation characteristics, from protective during the leaf-on season to harmful during the pollen season.

Data-Driven Framework for Understanding and Predicting Air Quality in Urban Areas.

Monitoring, predicting, and controlling the air quality in urban areas is one of the effective solutions for tackling the climate change problem. Leveraging the availability of big data in different domains like pollutant concentration, urban traffic, aerial imagery of terrains and vegetation, and weather conditions can aid in understanding the interactions between these factors and building a reliable air quality prediction model. This research proposes a novel cost-effective and efficient air quality modeling framework including all these factors employing state-of-the-art artificial intelligence techniques. The framework also includes a novel deep learning-based vegetation detection system using aerial images. The pilot study conducted in the UK city of Cambridge using the proposed framework investigates various predictive models ranging from statistical to machine learning and deep recurrent neural network models. This framework opens up possibilities of broadening air quality modeling and prediction to other domains like vegetation or green space planning or green traffic routing for sustainable urban cities. The research is mainly focused on extracting strong pieces of evidence which could be useful in proposing better policies around climate change.

Urban tree carbon density and CO2 equivalent of National Zoological Park, Delhi.

In a highly urbanized city like Delhi, the urban forest plays a vital role in climate change mitigation by capturing and storing carbon dioxide (CO2) from the atmosphere. Urban vegetation helps in increasing carbon sink and CO2 equivalent (CO2eq) and also provides other aesthetic and psychological environmental benefits. To understand how urban trees are vital for carbon sink, the present study aimed to quantify the carbon density and CO2eq in trees at National Zoological Park (NZP), New Delhi, a tropical semi-arid region of India. For this, we estimated tree biomass or dry matter content of 25 species with the help of allometric equations which are available in published literature and applicable for the tropical region. It was observed that the highest diameter at breast height (DBH) was contributed by Ficus sp. while the maximum density among adult tree species found in Albizia procera. The total mean dry matter content, C density, and CO2eq of NZP were 92.10 Mg ha-1, 43.61 Mg-C ha-1, and 168.83 Mg ha-1, respectively. The highest biomass, C density, and CO2eq obtained in the species of Ficus benghalensis followed by Ficus racemosa and Azadirachta indica. The data indicates that the trees having the capacity to store carbon are essential for the maintenance of a sustainable environment. Thus, the study suggests that there is a substantial scope to increase the carbon density and CO2eq in urban city through adopting various management strategies viz. afforestation and reforestation program on degraded and abandoned land to maintain a clean and sustainable environment.

Effects of Visual Attributes of Flower Borders in Urban Vegetation Landscapes on Aesthetic Preference and Emotional Perception.

The vegetation landscape in urban green space has been shown to provide great psychological benefits to people. Flower border is a well-designed small-scale vegetation landscape with the advantages of color and vegetation richness. This study focused on the effects of the visual attributes of flower borders on the aesthetic preference and emotional perception. The face recognition measurement method was used to obtain the emotional perception and the questionnaire survey method was used to measure the aesthetic preference. The results indicated the following: (1) regarding the 'color features' factor, high proportions of cool color and green vegetation significantly increased aesthetic preference and emotional valence, while the proportion of warm color had a negative effect on valence; (2) the 'visual attractiveness' (color brightness, and visual richness) and 'color configuration' (number of plant patches and number of color hues) factor was positively associated with aesthetic preference and emotional valence; (3) aesthetic preference was significantly related to emotional valence; (4) males expressed higher aesthetic preference and valence for flower border images than females. The results are expected to improve the aesthetic quality of flower borders and to promote public emotional health through the effective design of urban vegetation landscapes.

Effects of tree plantings and aspect ratios on pedestrian visual and thermal comfort using scaled outdoor experiments.

Urban trees ameliorate heat stress for urban dwellers. However, it is difficult to quantitatively assess the integrated impacts of tree planting and street layouts on visual and thermal comfort in simulations and urban field experiments. We conducted scaled outdoor experiments in Guangzhou to investigate the influence of tree plantings on pedestrian visual and thermal comfort in street canyons with various aspect ratios (H/W = 1, 2, 3; H = 1.2 m). We considered the effects of tree crown covers (big and small crown) and tree planting densities (ρ = 1, 0.5) on pedestrian illuminance level and two thermal comfort indices (Physiological Equivalent Temperature: PET and Index of Thermal Stress: ITS). When ρ = 1, trees in most cases reduce pedestrian illuminance (maximum 140.0klux) and improve visual comfort. Decreasing ρ from 1 to 0.5 increases the illuminance (maximum 179.5klux) in the streets with big crown trees (H/W = 1, 2) and in the street with small crown trees (H/W = 2). When ρ = 1 (H/W = 1, 2), big crown trees decrease the peak daytime PET (by about 4.0 °C) and ITS (by about 285 W). Small crown trees (ρ = 1, H/W = 1, 2) produce a warming effect on peak daytime PET (2.0-3.0 °C), but a reduction in ITS is observed when H/W = 2, 3. After reducing ρ from 1 to 0.5, big crown trees increase peak daytime thermal stress according to both indices when H/W = 1, 2. Small crown trees exhibit a similar PET cycle between ρ = 0.5 and ρ = 1 across various H/W, but their daytime reduction of ITS is less effective when ρ = 0.5 (H/W = 2). The discrepancies between PET and ITS are attributed to their different approaches to modelling radiation fluxes. The narrower the street, the lower the illuminance, PET, and ITS, while their increases caused by reduced ρ are limited in narrow streets. Our study informs some potential urban tree planting strategies and produces high-quality validation data for numerical simulations and theoretical models.

Environmental co-benefits of urban greening for mitigating heat and carbon emissions.

Urban greening has been as a popular and effective strategy for ameliorating urban thermal environment and air quality. Nevertheless, it remains an outstanding challenge for numerical urban models to disentangle and quantify the complex interplay between heat and carbon dynamics. In this study, we used a newly developed coupled urban canopy-carbon dynamics model to investigate the environmental co-benefits for mitigating urban heat stress as well as the reduction of carbon dioxide (CO2) emission. In particular, we evaluated the impact of specific components of urban greening, viz. fraction of the urban lawn, bare soil, tree coverage, and irrigation on heat and carbon fluxes in the built environment. The results of numerical simulations show that the expansion of urban green space, in general, leads to environmental cooling and reduced CO2 emission, albeit the efficacy varies for different vegetation types. In addition, adequate irrigation is essential to effect plant physiological functions for cooling and CO2 uptake, whereas further improvement becomes marginal with excessive irrigation. The findings of this study, along with its implications on environmental management, will help to promote sustainable urban development strategies for achieving desirable environmental co-benefits for urban residents and practitioners.

Geomatics and epidemiology: Associating oxidative stress and greenness in urban areas.

Green spaces may benefit human health mainly by mitigating noise and air pollution, promoting physical or social activities and improving mental health. Based on the influence that green space exposure seems to exert on Public Health and using a multidisciplinary approach, we investigated, the association between oxidative stress (OS) and green exposure in children. Overall, 207 subjects (10-13 yrs) living in Torino (NW- Italy) were involved in this study. Each participant provided a urinary sample, used to quantify a reliable OS biomarker (15-F2t-IsoP), and their residence addresses, used for geocoding. Green exposure was characterised by calculating i) the Soil Adjusted Vegetation Index (SAVI) within fixed buffers around each participant's home, using remotely-sensed data; ii) Tree Map accounting for evergreen/broadleaf species; iii) The percentage of green cover (PGC). Significant negative correlation (Pearson's r = -0.758, p < 0.001) between PGC and 15-F2t-IsoP was found. Greater SAVI was associated with lower OS (Pearson's r = -0.717, p < 0.001). Noticeably, evergreens seemed to determine a significant OS reduction compared to broadleaves (slope = -0.12 and -0.02, respectively; Warton-test F = 12.48, p = 0.0011). Finally, a spatial distribution of 15-F2t-IsoP estimates map, overlying with 2011 Census Data on same-aged dwellers of Torino, was generated. Predictive models accounting for green spaces influence on OS can be useful tool derived from geomatic employ in the Public Health field. Future developments of such a multidisciplinary approach should be considered in urban planning and policy-makers decisions to better define priority zones to requalify in urban settings.

Integrated impacts of tree planting and aspect ratios on thermal environment in street canyons by scaled outdoor experiments.

Urban tree planting has the potential to reduce urban heat island intensity and building energy consumption. However, the heterogeneity of cities makes it difficult to quantitatively assess the integrated impacts of tree planting and street layouts. Scaled outdoor experiments were conducted to investigate the influence of tree plantings on wind and thermal environments in two-dimensional (2D) north-south oriented street canyons with various aspect ratios (building height/street width, AR = H/W = 1, 2, 3; H = 1.2 m). The effects of tree species with similar leaf area index (C. kotoense, big crown; C. macrocarpa, small crown), tree planting densities (ρ = 1, 0.5), and arrangements (double-row, single-row) were considered. Vegetation reduces pedestrian-level wind speed by 29%-70%. For ρ = 1 and single-row arrangement, C. kotoense (big crown) has a better shading effect and decreases wall and air temperature during the daytime by up to 9.4 °C and 1.2 °C, respectively. In contrast, C. macrocarpa (small crown) leads to a temperature increase at the pedestrian level. Moreover, C. kotoense raises the air and wall temperature of the upper urban canopy layer and increases the street albedo during the daytime because of the solar radiation reflected by trees. C. kotoense/C. macrocarpa produces the maximum daytime cooling/warming and nighttime warming of air temperature when H/W = 2 owing to its weaker convective heat transfer. When H/W = 3, the building shade dominates the shading cooling and tree cooling is less significant. When ρ = 1, double-row trees (C. kotoense) reduce wall and air temperatures by up to 10.0 °C and 1.0 °C during the daytime. However, reducing ρ from 1 to 0.5 weakens the capacity of daytime cooling by C. kotoense and the warming effect by C. macrocarpa. Our study quantifies the influence of tree planting and aspect ratios on the thermal environment, which can provide meaningful references for urban tree planting and produce high-quality validation data for numerical modeling.

Dust deposition on vegetation leaves in Shanghai, China.

Urban vegetation can deposit dust to reduce pollution, and dust retention capacity of vegetation has become an important indicator for urban ecological construction. We selected five representative vegetation in Shanghai to explore the regularity of dust deposition on vegetation leaves. Due to the influence of leaf area and surface characteristics, the amount of dust deposition was significantly different to each vegetation; Vegetation shows different dust retention capacity under different pollution intensity, before this capacity reaches its limit, and it will increase with the increase of dust content in the environment; Furthermore, water content of leaves was an important factor affecting dust retention capacity by vegetation. There was a linear positive correlation between the two variables. Our work suggests that the dust retention capacity of vegetation leaves was affected by various factors, but it showed certain regularity, which can provide a scientific basis for the configuration of urban green plant species.

Investigation of the impacts of urban vegetation loss on the ecosystem service of air pollution mitigation in Karaj metropolis, Iran.

The present study aims to investigate the relationship between reduced air pollution and ecosystem services in Karaj metropolis, Iran. To the end, the trends in the concentrations of O3, NO2, CO, SO2, PM10, and PM2.5 as the main atmospheric pollutants of Karaj were studied. Five time series models of autoregressive (AR), moving average (MA), autoregressive moving average (ARMA), autoregressive integrated moving average (ARIMA), and seasonal autoregressive integrated moving average (SARIMA) were used to predict changes in air pollutant concentrations. Air pollution zoning is conducted via ArcGIS10.3 by using spline tension interpolation method. Then, normalized difference vegetation index (NDVI) was obtained from Landsat Thematic Mapper (TM) and Operational Land Imager (OLI) images to analyze vegetation dynamics as an index of ecosystem functioning. NDVI thresholds were selected to present guidelines for qualitative and quantitative changes in green cover and were divided into five different categories. Based on the results, AR (1) and ARIMA (1,2,1) were recognized as appropriate models for predicting the concentration of air pollutants in the study area. A decrease in very dense vegetation coverage and increase in poor vegetation areas, followed by an increase in air pollution, revealed that the loss of urban green coverage and decreased ecosystem services were positively related. Furthermore, the expansion of urban lands toward the north and the west from the baseline to future condition led to great changes in the land cover and losses in vegetation along these axes, which finally resulted in increased air pollution in these areas. Thus, the results of this study can be directly used in decision-making in the area of air pollution.

Multi-Agent-Based Urban Vegetation Design.

Urban vegetation is an essential element of the urban city pedestrian walkway. Despite city forest regulations and urban planning best practices, vegetation planning lacks clear comprehension and compatibility with other urban elements surrounding it. Urban planners and academic researchers currently devote vital attention to include most of the urban elements and their impact on the occupants and the environment in the planning stage of urban development. With the advancement in computational design, they have developed various algorithms to generate design alternatives and measure their impact on the environment that meets occupants' needs and perceptions of their city. In particular, multi-agent-based simulations show great promise in developing rule compliance with urban vegetation design tools. This paper proposed an automatic urban vegetation city rule compliance approach for pedestrian pathway vegetation, leveraging multi-agent system and algorithmic modeling tools. This approach comprises three modules: rule compliance (T-Rule), street vegetation design tool (T-Design), and multi-agent alternative generation (T-Agent). Notably, the scope of the paper is limited to trees, shrubbery, and seating area configurations in the urban pathway context. To validate the developed design tool, a case study was tested, and the vegetation design tool generated the expected results successfully. A questionnaire was conducted to give feedback on the use of the developed tool for enhancing positive experience of the developed tool. It is anticipated that the proposed tool has the potential to aid urban planners in decision-making and develop more practical vegetation planting plans compared with the conventional Two-Dimensional (2D) plans, and give the city occupants the chance to take part in shaping their city by merely selecting from predefined parameters in a user interface to generate their neighborhood pathway vegetation plans. Moreover, this approach can be extended to be embedded in an interactive map where city occupants can shape their neighborhood greenery and give feedback to urban planners for decision-making.

Comparative assessment of net CO2 exchange across an urbanization gradient in Korea based on eddy covariance measurements.

BACKGROUND: It is important to quantify changes in CO2 sources and sinks with land use and land cover change. In the last several decades, carbon sources and sinks in East Asia have been altered by intensive land cover changes due to rapid economic growth and related urbanization. To understand impact of urbanization on carbon cycle in the monsoon Asia, we analyze net CO2 exchanges for various land cover types across an urbanization gradient in Korea covering high-rise high-density residential, suburban, cropland, and subtropical forest areas. RESULTS: Our analysis demonstrates that the urban residential and suburban areas are constant CO2 sources throughout the year (2.75 and 1.02 kg C m-2 year-1 at the urban and suburban sites), and the net CO2 emission indicate impacts of urban vegetation that responds to the seasonal progression of the monsoon. However, the total random uncertainties of measurement are much larger in the urban and suburban areas than at the nonurban sites, which can make it challenging to obtain accurate urban flux measurements. The cropland and forest sites are strong carbon sinks because of a double-cropping system and favorable climate conditions during the study period, respectively (- 0.73 and - 0.60 kg C m-2 year-1 at the cropland and forest sites, respectively). The urban area of high population density (15,000 persons km-2) shows a relatively weak CO2 emission rate per capita (0.7 t CO2 year-1 person-1), especially in winter because of a district heating system and smaller traffic volume. The suburban area shows larger net CO2 emissions per capita (4.9 t CO2 year-1 person-1) because of a high traffic volume, despite a smaller building fraction and population density (770 persons km-2). CONCLUSIONS: We show that in situ flux observation is challenging because of its larger random uncertainty and this larger uncertainty should be carefully considered in urban studies. Our findings indicate the important role of urban vegetation in the carbon balance and its interaction with the monsoon activity in East Asia. Urban planning in the monsoon Asia must consider interaction on change in the monsoon activity and urban structure and function for sustainable city in a changing climate.

Heatwaves, hospitalizations for Alzheimer's disease, and postdischarge deaths: A population-based cohort study.

BACKGROUND: Available data on the effects of heatwaves on hospitalizations and postdischarge status of Alzheimer's disease patients are very scarce. METHODS: We used data from a retrospective cohort study which included Alzheimer's disease patients who were hospitalized from 1st January 2005 to 31st December 2013 in Brisbane, Australia, and died within two months after they were discharged. A time-stratified case-crossover design using conditional logistic regression was employed to quantify the effects of heatwaves on hospitalizations and postdischarge deaths due to Alzheimer's disease. A case-only design was used to assess the modification effects of age, sex, and community-level vegetation and Socio-Economic Indexes for Areas (SEIFA) on heatwave effects. RESULTS: There were 907 hospitalizations in the study period, and 307 patients died within two months after discharge. Hospitalizations and postdischarge deaths due to Alzheimer's disease increased by 51% (95% confidence interval (CI): 2%, 126%) and 269% (95% CI: 76%, 665%), respectively, during middle-intensity heatwaves (i.e., 95th percentile & ≥2 days). The magnitude of heatwave effect on postdischarge deaths increased dramatically when heatwave intensity increased from 95th percentile to 97th percentile. People who lived in communities with low-level vegetation were more vulnerable to heatwave effect on hospitalizations for Alzheimer's disease than those who lived in communities with high-level vegetation (relative risk: 3.05, 95% CI: 1.16, 7.98). CONCLUSION: Heatwaves increased the risk of hospitalizations for those living with Alzheimer's disease, and increased the risk of postdischarge deaths of Alzheimer's disease patients. Increasing urban greenness may ease heat-related Alzheimer's disease burden.

One-class land-cover classification using MaxEnt: the effect of modelling parameterization on classification accuracy.

Multiple-class land-cover classification approaches can be inefficient when the main goal is to classify only one or a few classes. Under this scenario one-class classification algorithms could be a more efficient alternative. Currently there are several algorithms that can fulfil this task, with MaxEnt being one of the most promising. However, there is scarce information regarding parametrization for performing land-cover classification using MaxEnt. In this study we aimed to understand how MaxEnt parameterization affects the classification accuracy of four different land-covers (i.e., built-up, irrigated grass, evergreen trees and deciduous trees) in the city of Santiago de Chile. We also evaluated if MaxEnt manual parameterization outperforms classification results obtained when using MaxEnt default parameters setting. To accomplish our objectives, we generated a set of 25,344 classification maps (i.e., 6,336 for each assessed land-cover), which are based on all the potential combination of 12 different classes of features restrictions, four regularization multipliers, four different sample sizes, three training/testing proportions, and 11 thresholds for generating the binary maps. Our results showed that with a good parameterization, MaxEnt can effectively classify different land covers with kappa values ranging from 0.68 for deciduous trees to 0.89 for irrigated grass. However, the accuracy of classification results is highly influenced by the type of land-cover being classified. Simpler models produced good classification outcomes for homogenous land-covers, but not for heterogeneous covers, where complex models provided better outcomes. In general, manual parameterization improves the accuracy of classification results, but this improvement will depend on the threshold used to generate the binary map. In fact, threshold selection showed to be the most relevant factor impacting the accuracy of the four land-cover classification. The number of sampling points for training the model also has a positive effect on classification results. However, this effect followed a logarithmic distribution, showing an improvement of kappa values when increasing the sampling from 40 to 60 points, but showing only a marginal effect if more than 60 sampling points are used. In light of these results, we suggest testing different parametrization and thresholds until satisfactory kappa or other accuracy metrics values are achieved. Our results highlight the huge potential that MaxEnt has a as a tool for one-class classification, but a good understanding of the software settings and model parameterization is needed to obtain reliable results.