Title: Occupational Exposure – A pilot study for construction projects

Air pollution from construction activities contribute to more than 500,000 annual deaths. PM2.5 is given off during construction activities, and it is harmful to humans and the environment. Unlike most workplaces, personnel on construction projects’ sites are exposed to dynamic environments with varying degree of occupational exposure.

It is therefore imperative to understand these dynamic exposures to mitigate mortality and low productivity from construction related air pollution. This proposal aims to reduce the indoor air pollution exposure of construction workers and personnel during the workday, and subsequently improve the working environment.

Besides supporting the University of Manchester’s (UoM) social responsibility goal, evidence from this pilot study will build on the highly successful recently concluded UoM GCRF funded SQUARE project in which portable emission monitors were used to acquire the COVID-19 lockdown and post-lockdown air quality data in Lagos, which, with a population of 17.5 m residents, is the largest city in Africa.

Data from the previous project presented for the first time a complete 12-month assessment of 6-species of pollutants [NO, NO2, O3, PM1, PM2.5, PM10] in Lagos, including the temporal effects and comparison with WHO legal limits. These were used to develop machine learning models that can be used to predict the air quality data in low resource regions without dedicated air quality monitors (Ejohwomu et al. 2022).


Ejohwomu, O.A.; Shamsideen Oshodi, O.; Oladokun, M.; Bukoye, O.T.; Emekwuru, N.; Sotunbo, A.; Adenuga, O. Modelling and Forecasting Temporal PM2.5 Concentration Using Ensemble Machine Learning Methods. Buildings 202212, 46. https://doi.org/10.3390/buildings12010046

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