FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3205939503> ?p ?o ?g. }

• W3205939503 endingPage "100989" @default.
• W3205939503 startingPage "100989" @default.
• W3205939503 abstract "Air pollution is a global geo-hazard with significant implications, including deterioration of health and premature death. Climatic variables such as temperature, rainfall, wind, and humidity impact air pollution by affecting the strength, transportation, and dispersion of pollutants in the atmosphere. Emerging data science tools, particularly Machine Learning (ML) big data analytics, are being utilized to predict air pollution intensity and frequency under varying climatic conditions for effective mitigation plans. However, comprehensive documentation of these digitalization approaches and outcomes in terms of correlating future air pollution with climate change remains scant. This study addresses this gap by systematically reviewing pertinent literature on climate change and air pollution studies. We also investigated the potentials of integrated spatial data science for spatial modelling and identifying cities vulnerable to air pollution hazards. Our findings show that climatic factors and seasonal variations are critical predictors of air quality in urban areas. A strong correlation exists between climate change and air quality, and air quality in urbanized regions is projected to deteriorate with climate change in the future. Therefore, climatic variables remain essential factors for the prediction of air quality. Also, air pollutants tend to have higher concentration in the warm season, making the consideration of seasonal changes crucial in air quality management. The study also revealed that machine learning algorithms such as random forest, gradient boosting machine, and classification and regression trees (CART) accurately predict air pollution hazard when integrated with spatial models. The detailed review of literature undertaken in this study provides a strong basis for the conclusion that the integration of spatial techniques and machine learning has the potential to improve air pollution prediction outcome and aid appropriate intervention initiatives by the stakeholders. Thus, emerging geospatial intelligence technologies and digital innovations particularly Artificial intelligence, machine learning and big data analytics that underpin the fourth industrial revolution (IR 4.0) can enhance existing early warning mechanisms and support a prompt and effective response to climate-change-induced air pollution, thereby fostering sustainable cities and societies." @default.
• W3205939503 created "2021-10-25" @default.
• W3205939503 creator A5017775227 @default.
• W3205939503 creator A5029465212 @default.
• W3205939503 creator A5040968468 @default.
• W3205939503 creator A5079592271 @default.
• W3205939503 date "2021-12-01" @default.
• W3205939503 modified "2023-10-01" @default.
• W3205939503 title "A review of the inter-correlation of climate change, air pollution and urban sustainability using novel machine learning algorithms and spatial information science" @default.
• W3205939503 cites W1967504029 @default.
• W3205939503 cites W1969296254 @default.
• W3205939503 cites W1976464776 @default.
• W3205939503 cites W1981338304 @default.
• W3205939503 cites W1996917229 @default.
• W3205939503 cites W2004554481 @default.
• W3205939503 cites W2024909040 @default.
• W3205939503 cites W2026727583 @default.
• W3205939503 cites W2032041768 @default.
• W3205939503 cites W2053255375 @default.
• W3205939503 cites W2057618554 @default.
• W3205939503 cites W2064003916 @default.
• W3205939503 cites W2064131631 @default.
• W3205939503 cites W2087972273 @default.
• W3205939503 cites W2089080766 @default.
• W3205939503 cites W2101167813 @default.
• W3205939503 cites W2107700650 @default.
• W3205939503 cites W2113714839 @default.
• W3205939503 cites W2117087906 @default.
• W3205939503 cites W2119219396 @default.
• W3205939503 cites W2132809245 @default.
• W3205939503 cites W2133169807 @default.
• W3205939503 cites W2135998216 @default.
• W3205939503 cites W2138637832 @default.
• W3205939503 cites W2138899384 @default.
• W3205939503 cites W2142827986 @default.
• W3205939503 cites W2154933777 @default.
• W3205939503 cites W2156531213 @default.
• W3205939503 cites W2169707207 @default.
• W3205939503 cites W2178756194 @default.
• W3205939503 cites W2189238739 @default.
• W3205939503 cites W2202178705 @default.
• W3205939503 cites W2218430543 @default.
• W3205939503 cites W2224566878 @default.
• W3205939503 cites W2239373335 @default.
• W3205939503 cites W2256261157 @default.
• W3205939503 cites W2281847536 @default.
• W3205939503 cites W2282992258 @default.
• W3205939503 cites W2293062392 @default.
• W3205939503 cites W2301202051 @default.
• W3205939503 cites W2316167246 @default.
• W3205939503 cites W2319635439 @default.
• W3205939503 cites W2321565276 @default.
• W3205939503 cites W2338667093 @default.
• W3205939503 cites W2339044324 @default.
• W3205939503 cites W2341826667 @default.
• W3205939503 cites W2433679148 @default.
• W3205939503 cites W2434774580 @default.
• W3205939503 cites W2462117221 @default.
• W3205939503 cites W2485441208 @default.
• W3205939503 cites W2494035975 @default.
• W3205939503 cites W2515646722 @default.
• W3205939503 cites W2519648711 @default.
• W3205939503 cites W2523199838 @default.
• W3205939503 cites W2542185977 @default.
• W3205939503 cites W2549517843 @default.
• W3205939503 cites W2551005902 @default.
• W3205939503 cites W2555513404 @default.
• W3205939503 cites W2588978790 @default.
• W3205939503 cites W2603613938 @default.
• W3205939503 cites W2606665849 @default.
• W3205939503 cites W2612864158 @default.
• W3205939503 cites W2725857760 @default.
• W3205939503 cites W2748314088 @default.
• W3205939503 cites W2767085346 @default.
• W3205939503 cites W2774119575 @default.
• W3205939503 cites W2782053432 @default.
• W3205939503 cites W2790987946 @default.
• W3205939503 cites W2792357166 @default.
• W3205939503 cites W2793997912 @default.
• W3205939503 cites W2794549909 @default.
• W3205939503 cites W2795955308 @default.
• W3205939503 cites W2796750419 @default.
• W3205939503 cites W2799476458 @default.
• W3205939503 cites W2803765368 @default.
• W3205939503 cites W2804053021 @default.
• W3205939503 cites W2804239627 @default.
• W3205939503 cites W2804841028 @default.
• W3205939503 cites W2810604373 @default.
• W3205939503 cites W2810892148 @default.
• W3205939503 cites W2838742489 @default.
• W3205939503 cites W2887397130 @default.
• W3205939503 cites W2889319740 @default.
• W3205939503 cites W2893552031 @default.
• W3205939503 cites W2897457583 @default.
• W3205939503 cites W2898461917 @default.
• W3205939503 cites W2908377374 @default.
• W3205939503 cites W2911424673 @default.
• W3205939503 cites W2913649977 @default.

• next