English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

According to the World Health Organization (WHO), air pollution kills sevenmillion people every year. Outdoor air pollution is a major environmentalhealth problem affecting low, middle, and high-income countries. In the pastfew years, the research community has explored IoT-enabled machine learningapplications for outdoor air pollution prediction. The general objective ofthis paper is to systematically review applications of machine learning andInternet of Things (IoT) for outdoor air pollution prediction and thecombination of monitoring sensors and input features used. Two researchquestions were formulated for this review. 1086 publications were collected inthe initial PRISMA stage. After the screening and eligibility phases, 37 paperswere selected for inclusion. A cost-based analysis was conducted on thefindings to highlight high-cost monitoring, low-cost IoT and hybrid enabledprediction. Three methods of prediction were identified: time series,feature-based and spatio-temporal. This review's findings identify majorlimitations in applications found in the literature, namely lack of coverage,lack of diversity of data and lack of inclusion of context-specific features.This review proposes directions for future research and underlines practicalimplications in healthcare, urban planning, global synergy and smart cities.

Applications of machine learning and IoT for Outdoor Air Pollution Monitoring and Prediction: A Systematic Literature Review