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A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Environmental Science and Engineering".

Deadline for manuscript submissions: closed (30 December 2019) | Viewed by 50243

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Special Issue Editor

Dr. Hone-Jay Chu

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Guest Editor

Department of Geomatics, National Cheng Kung University, No.1, University Road, Tainan City 701, Taiwan
Interests: space-time insights and data mining from remote sensing; big data; open data for environmental management and social sensing; environmental resilience; water and air quality mapping; groundwater; land cover and land use change; ISO metadata standards
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Environmental science reflects the interaction of human activities and the natural environment. Mapping spatial and temporal patterns of urban cities and understanding the causes and consequences of such details are critical tasks in the study of global change. As a result of the rapid development of information and data technology, environmental monitoring and social sensing in cities for land cover (use) change, air/water pollution, land subsidence, public health, crime, traffic information, and crowd movement have become important means of analyzing smart city and sustainable earth issues. This Special Issue of the International Journal of Environmental Research and Public Health offers an opportunity to publish high-quality multi-disciplinary environmental monitoring and social sensing research.

We will welcome papers related to environmental monitoring and social sensing. For this Special Issue, we invite submissions that provide spatio-temporal information on these environmental and human system dynamics, including GIS, GPS, remote sensing, UAV, social sensing, IoT, and big data. Implications for AI, data mining, and models of environmental monitoring and social sensing may also be addressed.

Dr. Hone-Jay Chu
Guest Editor

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Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Environmental monitoring
Social sensing
Remote sensing
Land cover and land use
Global change
Water
Air quality
GIS
Spatial interaction
Temporal activity pattern

Published Papers (14 papers)

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Research

24 pages, 11151 KiB

Open AccessArticle

Spatio-Temporal Variations of Satellite-Based PM_2.5 Concentrations and Its Determinants in Xinjiang, Northwest of China

by Wei Wang, Alim Samat, Jilili Abuduwaili and Yongxiao Ge

Int. J. Environ. Res. Public Health 2020, 17(6), 2157; https://doi.org/10.3390/ijerph17062157 - 24 Mar 2020

Cited by 23 | Viewed by 3348

Abstract

With the aggravation of air pollution in recent years, a great deal of research on haze episodes is mainly concentrated on the east-central China. However, fine particulate matter (PM_2.5) pollution in northwest China has rarely been discussed. To fill this gap, based on the standard deviational ellipse analysis and spatial autocorrelation statistics method, we explored the spatio-temporal variation and aggregation characteristics of PM_2.5 concentrations in Xinjiang from 2001 to 2016. The result showed that annual average PM_2.5 concentration was high both in the north slope of Tianshan Mountain and the western Tarim Basin. Furthermore, PM_2.5 concentrations on the northern slope of the Tianshan Mountain increased significantly, while showing an obviously decrease in the western Tarim Basin during the period of 2001–2016. Based on the result of the geographical detector method (GDM), population density was the most dominant factor of the spatial distribution of PM_2.5 concentrations (q = 0.550), followed by road network density (q = 0.423) and GDP density (q = 0.413). During the study period (2001–2016), the driving force of population density on the distribution of PM_2.5 concentrations showed a gradual downward trend. However, other determinants, like DEM (Digital elevation model), NSL (Nighttime stable light), LCT (Land cover type), and NDVI (Normalized Difference Vegetation Index), show significant increased trends. Therefore, further effort is required to reveal the role of landform and vegetation in the spatio-temporal variations of PM_2.5 concentrations. Moreover, the local government should take effective measures to control urban sprawl while accelerating economic development. Full article

(This article belongs to the Special Issue Spatio-Temporal Environmental Monitoring and Social Sensing)

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20 pages, 25879 KiB

Open AccessArticle

Expansion of Impervious Surfaces and Their Driving Forces in Highly Urbanized Cities in Kyrgyzstan

by Gulkaiyr Omurakunova, Anming Bao, Wenqiang Xu, Eldiiar Duulatov, Liangliang Jiang, Peng Cai, Farkhod Abdullaev, Vincent Nzabarinda, Khaydar Durdiev and Makhabat Baiseitova

Int. J. Environ. Res. Public Health 2020, 17(1), 362; https://doi.org/10.3390/ijerph17010362 - 05 Jan 2020

Cited by 17 | Viewed by 4378

Abstract

The expansion of urban areas due to population increase and economic expansion creates demand and depletes natural resources, thereby causing land use changes in the main cities. This study focuses on land cover datasets to characterize impervious surface (urban area) expansion in select cities from 1993 to 2017, using supervised classification maximum likelihood techniques and by quantifying impervious surfaces. The results indicate an increasing trend in the impervious surface area by 35% in Bishkek, 75% in Osh, and 15% in Jalal-Abad. The overall accuracy (OA) for the image classification of two different datasets for the three cities was between 82% and 93%, and the kappa coefficients (KCs) were approximately 77% and 91%. The Landsat images with other supplementary data showed positive urban growth in all of the cities. The GDP, industrial growth, and urban population growth were driving factors of impervious surface sprawl in these cities from 1993 to 2017.Landscape Expansion Index (LEI) results also provided good evidence for the change of impervious surfaces during the study period. The results emphasize the idea of applying future planning and sustainable urban development procedures for sustainable use of natural resources and their management, which will increase life quality in urban areas and environments. Full article

(This article belongs to the Special Issue Spatio-Temporal Environmental Monitoring and Social Sensing)

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14 pages, 2335 KiB

Open AccessArticle

Spectral Feature Selection Optimization for Water Quality Estimation

by Manh Van Nguyen, Chao-Hung Lin, Hone-Jay Chu, Lalu Muhamad Jaelani and Muhammad Aldila Syariz

Int. J. Environ. Res. Public Health 2020, 17(1), 272; https://doi.org/10.3390/ijerph17010272 - 30 Dec 2019

Cited by 13 | Viewed by 2856

Abstract

The spatial heterogeneity and nonlinearity exhibited by bio-optical relationships in turbid inland waters complicate the retrieval of chlorophyll-a (Chl-a) concentration from multispectral satellite images. Most studies achieved satisfactory Chl-a estimation and focused solely on the spectral regions from near-infrared (NIR) to red spectral bands. However, the optical complexity of turbid waters may vary with locations and seasons, which renders the selection of spectral bands challenging. Accordingly, this study proposes an optimization process utilizing available spectral models to achieve optimal Chl-a retrieval. The method begins with the generation of a set of feature candidates, followed by candidate selection and optimization. Each candidate links to a Chl-a estimation model, including two-band, three-band, and normalized different chlorophyll index models. Moreover, a set of selected candidates using available spectral bands implies an optimal composition of estimation models, which results in an optimal Chl-a estimation. Remote sensing images and in situ Chl-a measurements in Lake Kasumigaura, Japan, are analyzed quantitatively and qualitatively to evaluate the proposed method. Results indicate that the model outperforms related Chl-a estimation models. The root-mean-squared errors of the Chl-a concentration obtained by the resulting model (OptiM-3) improve from 11.95

mg \cdot m^{- 3}

to 6.37

mg \cdot m^{- 3}

, and the Pearson’s correlation coefficients between the predicted and in situ Chl-

a

improve from 0.56 to 0.89. Full article

(This article belongs to the Special Issue Spatio-Temporal Environmental Monitoring and Social Sensing)

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20 pages, 2611 KiB

Open AccessArticle

Global and Geographically and Temporally Weighted Regression Models for Modeling PM_2.5 in Heilongjiang, China from 2015 to 2018

by Qingbin Wei, Lianjun Zhang, Wenbiao Duan and Zhen Zhen

Int. J. Environ. Res. Public Health 2019, 16(24), 5107; https://doi.org/10.3390/ijerph16245107 - 14 Dec 2019

Cited by 36 | Viewed by 4093

Abstract

Objective: This study investigated the relationships between PM_2.5 and 5 criteria air pollutants (SO₂, NO₂, PM₁₀, CO, and O₃) in Heilongjiang, China, from 2015 to 2018 using global and geographically and temporally weighted regression models. Methods: Ordinary least squares regression (OLS), linear mixed models (LMM), geographically weighted regression (GWR), temporally weighted regression (TWR), and geographically and temporally weighted regression (GTWR) were applied to model the relationships between PM_2.5 and 5 air pollutants. Results: The LMM and all GWR-based models (i.e., GWR, TWR, and GTWR) showed great advantages over OLS in terms of higher model R² and more desirable model residuals, especially TWR and GTWR. The GWR, LMM, TWR, and GTWR improved the model explanation power by 3%, 5%, 12%, and 12%, respectively, from the R² (0.85) of OLS. TWR yielded slightly better model performance than GTWR and reduced the root mean squared errors (RMSE) and mean absolute error (MAE) of the model residuals by 67% compared with OLS; while GWR only reduced RMSE and MAE by 15% against OLS. LMM performed slightly better than GWR by accounting for both temporal autocorrelation between observations over time and spatial heterogeneity across the 13 cities under study, which provided an alternative for modeling PM_2.5. Conclusions: The traditional OLS and GWR are inadequate for describing the non-stationarity of PM_2.5. The temporal dependence was more important and significant than spatial heterogeneity in our data. Our study provided evidence of spatial–temporal heterogeneity and possible solutions for modeling the relationships between PM_2.5 and 5 criteria air pollutants for Heilongjiang province, China. Full article

(This article belongs to the Special Issue Spatio-Temporal Environmental Monitoring and Social Sensing)

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17 pages, 4618 KiB

Open AccessArticle

Impact of Urbanization on Ecosystem Health: A Case Study in Zhuhai, China

by Nan Cui, Chen-Chieh Feng, Rui Han and Luo Guo

Int. J. Environ. Res. Public Health 2019, 16(23), 4717; https://doi.org/10.3390/ijerph16234717 - 26 Nov 2019

Cited by 30 | Viewed by 3740

Abstract

The past decades have witnessed rapid urbanization around the world. This is particularly evident in Zhuhai City, given its status as one of the earliest special economic zones in China. After experiencing rapid urbanization for decades, the level of ecosystem health (ESH) in Zhuhai City has become a focus of attention. Assessments of urban ESH and spatial correlations between urbanization and ESH not only reveal the states of urban ecosystems and the extent to which urbanization affected these ecosystems, but also provide new insights into sustainable eco-environmental planning and resource management. In this study, we assessed the ESH of Zhuhai City using a selected set of natural, social and economic indicators. The data used include Landsat Thematic Mapper images and socio-economic data of 1999, 2005, 2009 and 2013. The results showed that the overall ESH value and ecosystem service function have been on the decline while Zhuhai City has continued to become more urbanized. The total ESH health level trended downward and the area ratio of weak and relatively weak health level increased significantly, while the areas of well and relatively well healthy state decreased since 1999. The spatial correlation analysis shows a distinct negative correlation between urbanization and ESH. The degree of negative correlation shows an upward trend with the processes of urban sprawl. The analysis results reveal the impact of urbanization on urban ESH and provide useful information for planners and environment managers to take measures to improve the health conditions of urban ecosystems. Full article

(This article belongs to the Special Issue Spatio-Temporal Environmental Monitoring and Social Sensing)

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15 pages, 2015 KiB

Open AccessArticle

Spatio-Temporal Prediction for the Monitoring-Blind Area of Industrial Atmosphere Based on the Fusion Network

by Yu-ting Bai, Xiao-yi Wang, Qian Sun, Xue-bo Jin, Xiao-kai Wang, Ting-li Su and Jian-lei Kong

Int. J. Environ. Res. Public Health 2019, 16(20), 3788; https://doi.org/10.3390/ijerph16203788 - 09 Oct 2019

Cited by 15 | Viewed by 2557

Abstract

The monitoring-blind area exists in the industrial park because of private interest and limited administrative power. As the atmospheric quality in the blind area impacts the environment management seriously, the prediction and inference of the blind area is explored in this paper. Firstly, the fusion network framework was designed for the solution of “Circumjacent Monitoring-Blind Area Inference”. In the fusion network, the nonlinear autoregressive network was set up for the time series prediction of circumjacent points, and the full connection layer was built for the nonlinear relation fitting of multiple points. Secondly, the physical structure and learning method was studied for the sub-elements in the fusion network. Thirdly, the spatio-temporal prediction algorithm was proposed based on the network for the blind area monitoring problem. Finally, the experiment was conducted with the practical monitoring data in an industrial park in Hebei Province, China. The results show that the solution is feasible for the blind area analysis in the view of spatial and temporal dimensions. Full article

(This article belongs to the Special Issue Spatio-Temporal Environmental Monitoring and Social Sensing)

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19 pages, 3476 KiB

Open AccessArticle

Determinants of Urban Expansion and Spatial Heterogeneity in China

by Ming Li, Guojun Zhang, Ying Liu, Yongwang Cao and Chunshan Zhou

Int. J. Environ. Res. Public Health 2019, 16(19), 3706; https://doi.org/10.3390/ijerph16193706 - 01 Oct 2019

Cited by 16 | Viewed by 3160

Abstract

China is the world’s largest developing country and its regions vary considerably. However, spatial heterogeneity in determinants of urban expansion in prefecture-level cities have not been identified. The present study explored the spatiotemporal characteristics of Chinese urban expansion and adopted a geographically weighted regression (GWR) method to determine this spatial heterogeneity. The results indicated that China experienced massive urban expansion during 1990–2015, with urban areas growing from 4.88 × 10⁴ km² to 1.06 × 10⁵ km², 46.42% of which was distributed in the eastern region. The results of the GWR model revealed the spatial heterogeneity in the determinants of urban expansion. Marketization was vital for urban expansion and had a stronger impact in the developed eastern and southern regions than in the less-developed northern and western regions. Globalization and decentralization bi-directionally affected urban expansion. The constraining effects of physical factors were limited and stronger in the developing northern region than in the developed southern region. Identifying the varying determinants of urban expansion is essential for policy-making in various regions. Full article

(This article belongs to the Special Issue Spatio-Temporal Environmental Monitoring and Social Sensing)

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17 pages, 3133 KiB

Open AccessArticle

Exploring Spatiotemporal Pattern of Grassland Cover in Western China from 1661 to 1996

by Fan Yang, Fanneng He, Shicheng Li and Meijiao Li

Int. J. Environ. Res. Public Health 2019, 16(17), 3160; https://doi.org/10.3390/ijerph16173160 - 29 Aug 2019

Cited by 11 | Viewed by 3143

Abstract

Historical grassland cover change is vital for global and regional environmental change modeling; however, in China, estimates of this are rare, and therefore, we propose a method to reconstruct grassland cover over the past 300 years. By synthesizing remote sensing-derived Chinese land use and land cover change (LULCC) data (1980–2015) and potential natural vegetation data simulated by the relationship between vegetation and environment, we first determined the potential extent of natural grassland vegetation (PENG) in the absence of human activities. Then we reconstructed grassland cover across western China between 1661 and 1996 at 10 km resolution by overlaying the Chinese historical cropland dataset (CHCD) over the PENG. As this land cover type has been significantly influenced by anthropogenic factors, the data show that the proportion of grassland in western China continuously decreased from 304.84 × 10⁶ ha in 1661 to 277.69 × 10⁶ ha in 1996. This reduction can be divided into four phases, comprising a rapid decrease between 1661 and 1724, a slow decrease between 1724 and 1873, a sharp decrease between 1873 and 1980, and a gradual increase since 1980. These reductions correspond to annual loss rates of 7.32 × 10⁴ ha, 2.90 × 10⁴ ha, 17.04 × 10⁴ ha, and −2.37 × 10⁴ ha, respectively. The data reconstructed here show that the decrease in grassland area between 1661 and 1724 was mainly limited to the Gan-Ning region (Gansu and Ningxia) and was driven by the early agricultural development policies of the Qing Dynasty. Grassland was extensively cultivated in northeastern China (Heilongjiang, Jilin, and Liaoning) and in the Xinjiang region between 1724 and 1980, a process which resulted from an exponential increase in immigrants to these provinces. The reconstruction results enable provide crucial data that can be used for modeling long-term climate change and carbon emissions. Full article

(This article belongs to the Special Issue Spatio-Temporal Environmental Monitoring and Social Sensing)

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16 pages, 3367 KiB

Open AccessArticle

Urban Road Network Expansion and Its Driving Variables: A Case Study of Nanjing City

by Ge Shi, Jie Shan, Liang Ding, Peng Ye, Yang Li and Nan Jiang

Int. J. Environ. Res. Public Health 2019, 16(13), 2318; https://doi.org/10.3390/ijerph16132318 - 30 Jun 2019

Cited by 44 | Viewed by 5359

Abstract

Developing countries such as China are undergoing rapid urban expansion and land use change. Urban expansion regulation has been a significant research topic recently, especially in Eastern China, with a high urbanization level. Among others, roads are an important spatial determinant of urban expansion and have significant influences on human activities, the environment, and socioeconomic development. Understanding the urban road network expansion pattern and its corresponding social and environmental effects is a reasonable way to optimize comprehensive urban planning and keep the city sustainable. This paper analyzes the spatiotemporal dynamics of urban road growth and uses spatial statistic models to describe its spatial patterns in rapid developing cities through a case study of Nanjing, China. A kernel density estimation model is used to describe the spatiotemporal distribution patterns of the road network. A geographically weighted regression (GWR) is applied to generate the social and environmental variance influenced by the urban road network expansion. The results reveal that the distribution of the road network shows a morphological character of two horizontal and one vertical concentration lines. From 2012 to 2016, the density of the urban road network increased significantly and developed some obvious focus centers. The development of the urban road network had a strong correlation with socioeconomic and environmental factors, which however, influenced it at different degrees in different districts. This study enhances the understanding of the effects of socio-economic and environmental factors on urban road network expansion, a significant indicator of urban expansion, in different circumstances. The study will provide useful understanding and knowledge to planning departments and other decision makers to maintain sustainable development. Full article

(This article belongs to the Special Issue Spatio-Temporal Environmental Monitoring and Social Sensing)

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19 pages, 2732 KiB

Open AccessArticle

Exploring Multidimensional Spatiotemporal Point Patterns Based on an Improved Affinity Propagation Algorithm

by Haifu Cui, Liang Wu, Zhanjun He, Sheng Hu, Kai Ma, Li Yin and Liufeng Tao

Int. J. Environ. Res. Public Health 2019, 16(11), 1988; https://doi.org/10.3390/ijerph16111988 - 04 Jun 2019

Cited by 6 | Viewed by 2553

Abstract

Affinity propagation (AP) is a clustering algorithm for point data used in image recognition that can be used to solve various problems, such as initial class representative point selection, large-scale sparse matrix calculations, and large-scale data with fewer parameter settings. However, the AP clustering algorithm does not consider spatiotemporal information and multiple thematic attributes simultaneously, which leads to poor performance in discovering patterns from massive spatiotemporal points (e.g., trajectory points). To resolve this issue, a multidimensional spatiotemporal affinity propagation (MDST-AP) algorithm is proposed in this study. First, the similarity of spatial and nonspatial attributes is measured in Gaussian kernel space instead of Euclidean space, which helps address the multidimensional linear inseparability problem. Then, the Davies-Bouldin (DB) index is applied to optimize the parameter value of the MDST-AP algorithm, which is applied to analyze road congestion in Beijing via taxi trajectories. Experiments on different datasets and algorithms indicated that the MDST-AP algorithm can process multidimensional spatiotemporal data points faster and more effectively. Full article

(This article belongs to the Special Issue Spatio-Temporal Environmental Monitoring and Social Sensing)

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20 pages, 5060 KiB

Open AccessArticle

Investigating the Spatiotemporal Variability and Driving Factors of Artificial Lighting in the Beijing-Tianjin-Hebei Region Using Remote Sensing Imagery and Socioeconomic Data

by Wanchun Leng, Guojin He and Wei Jiang

Int. J. Environ. Res. Public Health 2019, 16(11), 1950; https://doi.org/10.3390/ijerph16111950 - 01 Jun 2019

Cited by 9 | Viewed by 3196

Abstract

With rapid urbanization and economic development, artificial lighting at night brings convenience to human life but also causes a considerable urban environmental pollution issue. This study employed the Mann-Kendall non-parametric test, nighttime light indices, and the standard deviation method to investigate the spatio-temporal characteristics of artificial lighting in the Beijing-Tianjin-Hebei region. Moreover, nighttime light imagery from the Defense Meteorological Satellite Program Operational Linescan System, socioeconomic data, and high-resolution satellite images were combined to comprehensively explore the driving factors of urban artificial lighting change. The results showed the following: (1) Overall, there was an increasing trend in artificial lighting in the Beijing-Tianjin-Hebei region, which accounted for approximately 56.87% of the total study area. (2) The change in artificial lighting in the entire area was relatively stable. The artificial lighting in the northwest area changed faster than that in the southeast area, and the areas where artificial lighting changed the most were Beijing, Tianjin and Tangshan. (3) The fastest growth of artificial lighting was in Chengde and Zhangjiakou, where the rates of increase were 334% and 251%, respectively. The spatial heterogeneity of artificial lighting in economically developed cities was higher than that in economically underdeveloped cities such as Chengde and Zhangjiakou. (4) Multi-source data were combined to analyse the driving factors of urban artificial lighting in the entire area. The Average Population of Districts under City (R² = 0.77) had the strongest effect on artificial lighting. Total Passenger Traffic (R² = 0.54) had the most non-obvious effect. At different city levels, driving factors varied with differences of economy, geographical location, and the industrial structures of cities. Urban expansion, transportation hubs, and industries were the major reasons for the significant change in nighttime light. Urban artificial lighting represents a trend of overuse closely related to nighttime light pollution. This study of artificial lighting contributes to the rational planning of urban lighting systems, the prevention and control of nighttime light pollution, and the creation of liveable and ecologically green cities. Full article

(This article belongs to the Special Issue Spatio-Temporal Environmental Monitoring and Social Sensing)

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18 pages, 4307 KiB

Open AccessArticle

The Response of Net Primary Production to Climate Change: A Case Study in the 400 mm Annual Precipitation Fluctuation Zone in China

by Yang Li and Yaochen Qin

Int. J. Environ. Res. Public Health 2019, 16(9), 1497; https://doi.org/10.3390/ijerph16091497 - 27 Apr 2019

Cited by 12 | Viewed by 3185

Abstract

The regions in China that intersect the 400 mm annual precipitation line are especially ecologically sensitive and extremely vulnerable to anthropogenic activities. However, in the context of climate change, the response of vegetation Net Primary Production (NPP) in this region has not been scientifically studied in depth. NPP suffers from the comprehensive effect of multiple climatic factors, and how to eliminate the effect of interfering variables in the correlation analysis of NPP and target variables (temperature or precipitation) is the major challenge in the study of NPP influencing factors. The correlation coefficient between NPP and target variable was calculated by ignoring other variables that also had a large impact on NPP. This increased the uncertainty of research results. Therefore, in this study, the second-order partial correlation analysis method was used to analyze the correlation between NPP and target variables by controlling other variables. This can effectively decrease the uncertainty of analysis results. In this paper, the univariate linear regression, coefficient of variation, and Hurst index estimation were used to study the spatial and temporal variations in NPP and analyze whether the NPP seasonal and annual variability will persist into the future. The results show the following: (i) The spatial distribution of NPP correlated with precipitation and had a gradually decreasing trend from southeast to northwest. From 2000 to 2015, the NPP in the study area had a general upward trend, with a small variation in its range. (ii) Areas with negative partial correlation coefficients between NPP and precipitation are consistent with the areas with more abundant water resources. The partial correlation coefficient between the NPP and the Land Surface Temperature (LST) was positive for 52.64% of the total study area. Finally, the prediction of the persistence of NPP variation into the future showed significant differences on varying time scales. On an annual scale, NPP was predicted to persist for 46% of the study area. On a seasonal scale, NPP in autumn was predicted to account for 49.92%, followed by spring (25.67%), summer (13.40%), and winter (6.75%). Full article

(This article belongs to the Special Issue Spatio-Temporal Environmental Monitoring and Social Sensing)

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12 pages, 2473 KiB

Open AccessArticle

Asian Culturally Specific Predictors in a Large-Scale Land Use Regression Model to Predict Spatial-Temporal Variability of Ozone Concentration

by Chin-Yu Hsu, Jhao-Yi Wu, Yu-Cheng Chen, Nai-Tzu Chen, Mu-Jean Chen, Wen-Chi Pan, Shih-Chun Candice Lung, Yue Leon Guo and Chih-Da Wu

Int. J. Environ. Res. Public Health 2019, 16(7), 1300; https://doi.org/10.3390/ijerph16071300 - 11 Apr 2019

Cited by 25 | Viewed by 3450

Abstract

This paper developed a land use regression (LUR) model to study the spatial-temporal variability of O₃ concentrations in Taiwan, which has typical Asian cultural characteristics with diverse local emission sources. The Environmental Protection Agency’s (EPA) data of O₃ concentrations from 2000 and 2013 were used to develop this model, while observations from 2014 were used as the external data verification to assess model reliability. The distribution of temples, cemeteries, and crematoriums was included for a potential predictor as an Asian culturally specific source for incense and joss money burning. We used stepwise regression for the LUR model development, and applied 10-fold cross-validation and external data for the verification of model reliability. With the overall model R² of 0.74 and a 10-fold cross-validated R² of 0.70, this model presented a mid-high prediction performance level. Moreover, during the stepwise selection procedures, the number of temples, cemeteries, and crematoriums was selected as an important predictor. By using the long-term monitoring data to establish an LUR model with culture specific predictors, this model can better depict O₃ concentration variation in Asian areas. Full article

(This article belongs to the Special Issue Spatio-Temporal Environmental Monitoring and Social Sensing)

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12 pages, 36711 KiB

Open AccessArticle

Multi-Parameter Relief Map from High-Resolution DEMs: A Case Study of Mudstone Badland

by Hone-Jay Chu, Yi-Chin Chen, Muhammad Zeeshan Ali and Bernhard Höfle

Int. J. Environ. Res. Public Health 2019, 16(7), 1109; https://doi.org/10.3390/ijerph16071109 - 28 Mar 2019

Cited by 6 | Viewed by 4046

Abstract

Topographic parameters of high-resolution digital elevation models (DEMs) with meter to sub-meter spatial resolution, such as slope, curvature, openness, and wetness index, show the spatial properties and surface characterizations of terrains. The multi-parameter relief map, including two-parameter (2P) or three-parameter (3P) information, can visualize the topographic slope and terrain concavities and convexities in the hue, saturation, and value (HSV) color system. Various combinations of the topographic parameters can be used in the relief map, for instance, using wetness index for upstream representation. In particular, 3P relief maps are integrated from three critical topographic parameters including wetness or aspect, slope, and openness data. This study offers an effective way to explore the combination of topographic parameters in visualizing terrain features using multi-parameter relief maps in badlands and in showing the effects of smoothing and parameter selection. The multi-parameter relief images of high-resolution DEMs clearly show micro-topographic features, e.g., popcorn-like morphology and rill. Full article

(This article belongs to the Special Issue Spatio-Temporal Environmental Monitoring and Social Sensing)

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