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Abstrakty
. Increasing amounts of rapidly growing data are the driving force behind proposing and automating new processing, enabling the extraction of useful information from data. One of such possibilities is determining trends to consider in terms of time and space. Thus far, the analysis of these aspects has been separate and lacked automated tools. Therefore, the authors proposed, implemented, and tested a tool for analyzing spatio-temporal linear trends. The tool was tested on PM10 concentration data in the years 2000–2018. The results, presented as cartographic visualization, were then evaluated, both in terms of time and space. The proposed approach facilitates analyzing spatio-temporal trends and assessing their accuracy; it can be developed using other types of analyzed trends or considering additional factors that influence the trend by using cokriging.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
37--48
Opis fizyczny
Bibliogr. 31 poz., mapy, rys., tab., wykr.
Twórcy
autor
- Warsaw University of Technology, Faculty of Geodesy and Cartography
autor
- Warsaw University of Technology, Faculty of Geodesy and Cartography
autor
- Warsaw University of Technology, Faculty of Geodesy and Cartography
Bibliografia
- Bai, K., Ma, M., Chang, N. B., & Gao, W. (2019). Spatiotemporal trend analysis for fine particulate matter concentrations in China using high-resolution satellite-derived and ground-measured PM2.5 data. Journal of Environmental Management, 233, 530–542. https://doi.org/10.1016/j.jenvman.2018.12.071
- Diop, L., Bodian, A., & Diallo, D. (2016). Spatiotemporal trend analysis of the mean annual rainfall in Senegal. European Scientific Journal, 12(12), 231. https://doi.org/10.19044/esj.2016.v12n12p231
- Gao, S., Wang, Y., Shan, M., Teng, Y., Hong, N., Sun, Y., Mao, J., Ma, Z., Xiao, J., Azzi, M., Chen, L., & Zhang, H. (2020). Wind-tunnel and modelled PM10 emissions and dust concentrations from agriculture soils in Tianjin, Northern China. Aeolian Research, 42, 100562. https://doi.org/10.1016/j.aeolia.2019.100562
- Gąsiorowski, J. (2015). Szeregi czasowo-przestrzenne – analiza trendu. In A. Fiedukowicz, J. Gąsiorowski, & R. Olszewski (Eds.), Wybrane metody eksploracyjnej analizy danych przestrzennych (spatial data mining) (pp. 97–104). Wydział Geodezji i Kartografii Politechniki Warszawskiej.
- Główny Inspektorat Ochrony Środowiska. (n.d.). Ocena jakości powietrza – bieżące dane pomiarowe. Retrieved March 2019, from http://powietrze.gios.gov.pl
- Główny Inspektorat Ochrony Środowiska. (2011). Analiza stanu zanieczyszczenia powietrza pyłem PM10 i PM2,5 z uwzględnieniem składu chemicznego pyłu oraz wpływu źródeł naturalnych. Zabrze.
- Gupta, N., Banerjee,A., & Gupta, S. K. (2021). Spatio-temporal trend analysis of climatic variables over Jharkhand, India. Earth Systems and Environment, 5(1), 71–86. https://doi.org/10.1007/s41748-021-00204-x
- Hadi, S. J., & Tombul, M. (2018). Long-term spatiotemporal trend analysis of precipitation and temperature over Turkey. Meteorological Applications, 25(3), 445–455. https://doi.org/10.1002/met.1712
- Jackowska, M. (2020). Koncepcja sposobów analizy trendów szeregów czasowych zróżnicowanych przestrzennie, wraz z oceną jakości dokonywanych predykcji [Master’s thesis]. Warsaw University of Technology.
- Kamarehie, B., Ghaderpoori, M., Jafari,A., Karami, M., Mohammadi, A., Azarshab, K., Ghaderpoury, A., & Noorizadeh, N. (2017). Estimation of health effects (morbidity and mortality) attributed to PM10 and PM2.5 exposure using an air quality model in Bukan city, from 2015-2016 exposure using air quality model. Environmental Health Engineering and Management, 4(3), 137–142. https://doi.org/10.15171/EHEM.2017.19
- Kiersztyn, A. (2013). Analiza szeregów czasowych. https://pracownik.kul.pl/files/12167/public/analiza_szeregow_czasowych/AnalizaSzeregowCzasowychKonwersatorium.pdf
- Kirk, R. W., Bolstad, P. V., & Manson, S. M. (2012). Spatio-temporal trend analysis of long-term development patterns (1900–2030) in a Southern Appalachian County. Landscape and Urban Planning, 104(1), 47–58. https://doi.org/10.1016/j.landurbplan.2011.09.008
- Kołacz, A. M. (2021). Mapa i animacja kartograficzna wybranych zanieczyszczeń powietrza na terenie Polski [Engineer’s thesis]. Warsaw University of Technology.
- Kyriakidis, P. C., & Journel, A. G. (1999). Geostatistical space-time models: a review. Mathematical Geology, 31(6), 651–684. https://doi.org/10.1023/A:1007528426688
- Kyriakidis, P. C., Miller, N. L., & Kim, J. (2004). A spatial time series framework for simulating daily precipitation at regional scales. Journal of Hydrology, 297(1-4), 236–255. https://doi.org/10.1016/j.jhydrol.2004.04.022
- Lam, N. S. N. (1983). Spatial interpolation methods: a review. The American Cartographer, 10(2), 129–150. https://doi.org/10.1559/152304083783914958
- Li, J., & Heap, A. D. (2008). Spatial Interpolation Methods: A Review for Environmental Scientists. Geoscience Australia, Record 2008/23. http://www.ga.gov.au/webtemp/image_cache/GA12526.pdf
- Li, J., & Heap, A. D. (2014). Spatial interpolation methods applied in the environmental sciences: a review. Environmental Modelling & Software, 53, 173–189. https://doi.org/10.1016/j.envsoft.2013.12.008
- Lin, Z., Mo, X., Li, H. [Hong-xuan], & Li, H. [Hai-bin] (2002). Comparison of three spatial interpolation methods for climate variables in China. Acta Geographica Sinica, 57(1), 47–56.
- Luo, P., He, B., Takara, K., Razafindrabe, B. H. N., Nover, D., & Yamashiki, Y. (2011). Spatiotemporal trend analysis of recent river water quality conditions in Japan. Journal of Environmental Monitoring, 13(10), 2819–2829. https://doi.org/10.1039/c1em10339c
- Maji, K. J., Dikshit, A. K., & Deshpande, A. (2017). Disability-adjusted life years and economic cost assessment of the health effects related to PM2.5 and PM10 pollution in Mumbai and Delhi, in India from 1991 to 2015. Environmental Science and Pollution Research International, 24(5), 4709–4730. https://doi.org/10.1007/s11356-016-8164-1
- Malska, W., & Wachta, H. (2015). Wykorzystanie modelu ARIMA do analizy szeregu czasowego. Zeszyty Naukowe Politechniki Rzeszowskiej. Elektrotechnika, 34 [292](3), 23–30.
- Muhire, I., & Ahmed, F. (2015). Spatio-temporal trend analysis of precipitation data over Rwanda. South African Geographical Journal, 97(1), 50–68. https://doi.org/10.1080/03736245.2014.924869
- Mukherjee, A., & Agrawal, M. (2017). World air particulate matter: Sources, distribution and health effects. Environmental Chemistry Letters, 15(2), 283–309. https://doi.org/10.1007/s10311-017-0611-9
- Shumway, R. H., & Stoffer, D. S. (2017a). Characteristics of time series. In R. H. Shumway & D. S. Stoffer (Eds.), Springer Texts in Statistics. Time Series Analysis and Its Applications (pp. 1–44). Springer International Publishing.
- Shumway, R. H., & Stoffer, D. S. (2017b). Time series regression and exploratory data analysis. In R. H. Shumway & D. S. Stoffer (Eds.), Springer Texts in Statistics. Time Series Analysis and Its Applications (pp. 45–74). Springer International Publishing.
- Ścibor, M., Bokwa, A., & Balcerzak, B. (2020). Impact of wind speed and apartment ventilation on indoor concentrations of PM10 and PM2.5 in Kraków, Poland. Air Quality, Atmosphere & Health, 13(5), 553–562. https://doi.org/10.1007/s11869-020-00816-8
- Talchabhadel, R., Karki, R., Thapa, B. R., Maharjan, M., & Parajuli, B. (2018). Spatio-temporal variability of extreme precipitation in Nepal. International Journal of Climatology, 38(11), 4296–4313. https://doi.org/10.1002/joc.5669
- Tobler, W. R. (1970). A computer movie simulating urban growth in the Detroit region. Economic Geography, 46, 234. https://doi.org/10.2307/143141
- Wang, Y., Liu, Y., Struthers, J., & Lian, M. (2021). Spatiotemporal characteristics of the covid-19 epidemic in the United States. Clinical Infectious Diseases, 72(4), 643–651. https://doi.org/10.1093/cid/ciaa934
- World Health Organization (2013). Review of evidence on health aspects of air pollution: REVIHAAP project: technical report (No. WHO/EURO: 2013- 2663-42419-58845). World Health Organization. Regional Office for Europe.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b042e99d-ceec-44e0-a1bc-c73a876a1b5d
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