Spatiotemporal analysis of air pollution and its application in public health / edited by Lixin Li, Xiaolu Zhou, Weitian Tong.
| Other author | Li, Lixin. |
| Other author | Zhou, Xiaolu (Editor) |
| Other author | Tong, Weitian. |
| Format | Electronic |
| Publication Info | San Diego, CA : Elsevier, [2020] |
| Description | xii, 321 pages : illustrations (some color) ; 24 cm |
| Supplemental Content | Full text available from eBook - Environmental Science 2019 |
| Supplemental Content | Full text available from eBooks on EBSCOhost |
| Subjects |
| Contents | Introduction to spatiotemporal variations of ambient air pollutants and related public health impacts -- Statistical analysis for air pollution data -- Case study: does PM 2.5 contribute to the incidence of lung and bronchial cancers in the United States -- Bayesian hierarchical modeling for the linkages between air pollution and population heatlh -- Machine learning for spatiotemporal big data in air pollution -- Integrate machine learning and geostatistics for high-resolution mapping of ground-level PM 2.5 concentrations -- Spatiotemporal interpolation methods for air pollution -- Sensing air quality: spatiotemporal interpolation and visualization of real-time air pollution data for the contiguous United States -- Assessment methods for air pollution exposure -- Applying LUR model to estimate spaital variation of PM 2.5 in the Greater Bay Area, China -- Analysis of exposure to ambient air pollution: case study of the link between environmental exposure and children's school performance in Memphis, TN -- Concentrating risk? The geographic concentration of health risk from industrial air toxics across America -- Travel-related exposure to air pollution and its socio-environmental inequalities: evidence from a week-long GPS-based travel diary dataset. |
| Bibliography note | Includes bibliographical references and index. |
| Access restriction | Available only to authorized users. |
| Technical details | Mode of access: World Wide Web |
| Issued in other form | Ebook version : 9780128165263 |
| Genre/form | Electronic books. |
| LCCN | 2019955737 |
| ISBN | 9780128158227 (paperback) |
| ISBN | 0128158220 (paperback) |
| ISBN | (ePub ebook) |
Availability
| Library | Location | Call Number | Status | Item Actions |
|---|---|---|---|---|
| Electronic Resources | ✔ Available |