In silico study of possible roles of relative humidity in chemical speciation on ambient particulate matter using PHREEQC

There is increasing concern about the health impacts of ambient Particulate Matter (PM) exposure. Traditional monitoring networks, because of their sparseness, cannot provide sufficient spatial-temporal measurements characteristic of ambient PM. Recent studies have shown portable low-cost devices (e.g., optical particle counters, OPCs) can help address this issue; however, their application under ambient conditions can be affected by high relative humidity (RH) conditions. Here, we show how, by exploiting the measured particle size distribution information rather than PM as has been suggested elsewhere, a correction can be derived which not only significantly improves sensor performance but which also retains fundamental information on particle composition. A particle size distribution–based correction algorithm, founded on κ -Köhler theory, was developed to account for the influence of RH on sensor measurements. The application of the correction algorithm, which assumed physically reasonable κ values, resulted in a significant improvement, with the overestimation of PM measurements reduced from a factor of ~5 before correction to 1.05 after correction. We conclude that a correction based on particle size distribution, rather than PM mass, is required to properly account for RH effects and enable low cost optical PM sensors to provide reliable ambient PM measurements.

Download Full-text

Physicochemical Characterization of Air Particulate Matter in Medellin City and Its Use in an In-silico Study of the Effect of a PM Component on Cardiac Electrical Activity

Proceedings of the International Workshop on Materials, Chemistry and Engineering ◽

10.5220/0007443106630669 ◽

2018 ◽

Author(s):

R. Buitrago-Sierra ◽

C. Tobon ◽

L. C. Palacio ◽

J. Saiz

Keyword(s):

Particulate Matter ◽

Electrical Activity ◽

In Silico ◽

Physicochemical Characterization ◽

Air Particulate Matter ◽

Cardiac Electrical Activity ◽

Air Particulate ◽

In Silico Study

Download Full-text

Association between COVID-19, mobility and environment in São Paulo, Brazil

10.1101/2021.02.08.21250113 ◽

2021 ◽

Author(s):

Sergio Ibarra-Espinosa ◽

Edmilson Dias de Freitas ◽

Karl Ropkins ◽

Francesca Dominici ◽

Amanda Rehbein

Keyword(s):

Air Pollution ◽

Particulate Matter ◽

Relative Humidity ◽

Residential Mobility ◽

Additive Model ◽

Sao Paulo ◽

São Paulo ◽

Ambient Particulate Matter ◽

Mobility Index ◽

Small Increments

AbstractBackgroundBrazil, the country most impacted by the coronavirus disease 2019 (COVID-19) on the southern hemisphere, use intensive care admissions per day, mobility and other indices to control quarantines and prevent the transmissions of SARS-CoV2.In this study we quantified the associations between residential mobility index (RMI), air pollution, meteorology, and daily cases and deaths of COVID-19 in São Paulo, BrazilObjectivesTo estimate the associations between daily residential mobility index (RMI), air pollution, and meteorology, and daily cases and deaths for COVID-19 in São Paulo, Brazil.MethodsWe applied a semiparametric generalized additive model (GAM) to estimate: 1) the association between residential mobility index and cases and deaths due to COVID-19, accounting for ambient particulate matter (PM2.5), ozone (O3), relative humidity, temperature and delayed exposure between 3-21 days and 2) the association between exposure to for ambient particulate matter (PM2.5), ozone (O3), accounting for relative humidity, temperature and mobility.ResultsWe found an RMI of 45.28% results in 1,212 cases (95% CI: 1,189 to 1,235) and 44 deaths (95% CI: 40 to 47). Reducing mobility 5% would avoid 438 cases and 21 deaths. Also, we found that an increment of 10 μg·m-3 of PM2.5 risk of 1.140 (95% CI: 1.021 to 1.274) for cases and of 1.086 (95% CI: 1.008 to 1.170) for deaths, while O3 produces a relative risk of 1.075 (95% CI: 1.006 to 1.150) for cases and 1.063 (95% CI: 1.006 to 1.124) for deaths, respectively.DiscussionWe compared our results with observations and literature review, finding well agreement. These results implicate that authorities and policymakers can use such mobility indices as tools to support social distance activities and assess their effectiveness in the coming weeks and months. Small increments of air pollution pose a risk of COVID-19 cases.ConclusionSpatial distancing is a determinant factor to control cases and deaths for COVID-19. Small increments of air pollution result in a high number of COVID-19 cases and deaths. PM2.5 has higher relative risks for COVID-19 than O3.

Download Full-text