Effect of ambient air pollution and meteorological factors on the potential transmission of COVID-19 in Turkey.

There is a need to improve the understanding of air quality parameters and meteorological conditions on the transmission of SARS-CoV-2 in different regions of the world. In this preliminary study, we explore the relationship between short-term air quality (nitrogen dioxide (NO2), sulfur dioxide (SO2), ozone (O3), and particulate matter (PM2.5, PM10)) exposure, temperature, humidity, and wind speed on SARS-CoV-2 transmission in 41 cities of Turkey with reported weekly cases from February 8 to April 2, 2021. Both linear and non-linear relationships were explored. The nonlinear association between weekly confirmed cases and short-term exposure to predictor factors was investigated using a generalized additive model (GAM). The preliminary results indicate that there was a significant association between humidity and weekly confirmed COVID-19 cases. The cooler temperatures had a positive correlation with the occurrence of new confirmed cases. The low PM2.5 concentrations had a negative correlation with the number of new cases, while reducing SO2 concentrations may help decrease the number of new cases. This is the first study investigating the relationship between measured air pollutants, meteorological factors, and the number of weekly confirmed COVID-19 cases across Turkey. There are several limitations of the presented study, however, the preliminary results show that there is a need to understand the impacts of regional air quality parameters and meteorological factors on the transmission of the virus.

The impacts of COVID-19 lockdown on PM10 and SO2 concentrations and association with human mobility across Turkey.

The SARS-CoV-2 virus pandemic (COVID-19) has caused 2.25 million deaths worldwide by February 3, 2021 (JHU, 2021) and still causing severe health and economic disruptions with increasing rates. This study investigates the impact of lockdown measures on ambient air pollution and its association with human mobility in 81 cities of Turkey. We conducted a countrywide analysis using PM10 and SO2 measurement data by the Turkish Ministry of Environment and Urbanization and mobility data derived from cellular device movement by Google. We observed the most significant change in April 2020. PM10 and SO2 concentrations were lower in 67% and 59% of the cities, respectively in April 2020 compared to the previous five years (2015-2019). The correlation results show that Restaurant/Café, Transit, and Workplaces mobility is significantly correlated with PM10 and SO2 concentration levels in Turkey. This study is the first step of a long-term investigation to understand the air quality impacts on population susceptibility to COVID-19.

A Hybrid Bayesian Network Framework for Risk Assessment of Arsenic Exposure and Adverse Reproductive Outcomes.

Arsenic contamination of drinking water affects more than 137 million people and has been linked to several adverse health effects. The traditional toxicological approach, "dose-response" graphs, are limited in their ability to unveil the relationships between potential risk factors of arsenic exposure for adverse human health outcomes, which are critically important to understanding the risk at low exposure levels of arsenic. Therefore, to provide insight on the potential interactions of different variables of the arsenic exposure network, this study characterizes the risk factors by developing a hybrid Bayesian Belief Network (BBN) model for health risk assessment. The results show that the low inorganic arsenic concentration increases the risk of low birth weight even for low gestational age scenarios. While increasing the mother's age does not increase the low birthweight risk, it affects the distribution between other categories of baby weight. For low MMA% (<4%) in the human body, increasing gestational age decreases the risk of having low birthweight. The proposed BBN model provides 82% sensitivity and 72% specificity in average for different states of birthweight.

Bayesian network-based framework for exposure-response study design and interpretation.

Conventional environmental-health risk-assessment methods are often limited in their ability to account for uncertainty in contaminant exposure, chemical toxicity and resulting human health risk. Exposure levels and toxicity are both subject to significant measurement errors, and many predicted risks are well below those distinguishable from background incident rates in target populations. To address these issues methods are needed to characterize uncertainties in observations and inferences, including the ability to interpret the influence of improved measurements and larger datasets. Here we develop a Bayesian network (BN) model to quantify the joint effects of measurement errors and different sample sizes on an illustrative exposure-response system. Categorical variables are included in the network to describe measurement accuracies, actual and measured exposures, actual and measured response, and the true strength of the exposure-response relationship. Network scenarios are developed by fixing combinations of the exposure-response strength of relationship (none, medium or strong) and the accuracy of exposure and response measurements (low, high, perfect). Multiple cases are simulated for each scenario, corresponding to a synthetic exposure response study sampled from the known scenario population. A learn-from-cases algorithm is then used to assimilate the synthetic observations into an uninformed prior network, yielding updated probabilities for the strength of relationship. Ten replicate studies are simulated for each scenario and sample size, and results are presented for individual trials and their mean prediction. The model as parameterized yields little-to-no convergence when low accuracy measurements are used, though progressively faster convergence when employing high accuracy or perfect measurements. The inferences from the model are particularly efficient when the true strength of relationship is none or strong with smaller sample sizes. The tool developed in this study can help in the screening and design of exposure-response studies to better anticipate where such outcomes can occur under different levels of measurement error. It may also serve to inform methods of analysis for other network models that consider multiple streams of evidence from multiple studies of cumulative exposure and effects.

The Impacts on Greenhouse Gases Emission during the COVID-19 lockdown in the US: An Economic Input-Output Life Cycle Assessment.

The SARS-CoV-2 virus pandemic (COVID-19) is causing disruptions to energy, finance, tourism, and trade industries all around the world. These disruptions are the result of quarantining and lockdowns that cause reductions in production and consumptions. This change in production and consumption rates has environmental consequences. This study investigates the environmental effects of COVID-19 lockdown in the United States by Input-Output Life Cycle Assessment (IO-LCA) approach. The analysis is based on extraction of economic data in the US. The simulated results are based on different durations and strategies of lockdown measures. Among all industrial categories, utilities, which include power generation and supply, water supply, and natural gas supply sectors, saw the most significant reductions by approximately 110 kt CO2-eq in the first quarter and 265 kt CO2-eq in the second quarter of 2020. The assessed reductions were the results of both direct emission reductions caused by the shutdown of certain industries and also indirect emission reductions from upstream industries. The proposed methodology provides an effective guideline to predict the greenhouse gases emissions, which can be used as a prediction method for different regions in the world.