A causal machine-learning framework for studying policy impact on air pollution: a case study in COVID-19 lockdowns.

When studying the impact of policy interventions or natural experiments on air pollution, such as new environmental policies or the opening or closing of an industrial facility, careful statistical analysis is needed to separate causal changes from other confounding factors. Using COVID-19 lockdowns as a case study, we present a comprehensive framework for estimating and validating causal changes from such perturbations. We propose using flexible machine learning-based comparative interrupted time series (CITS) models for estimating such a causal effect.

The Association Between Personal Air Pollution Exposures and Fractional Exhaled Nitric Oxide (FeNO): A Systematic Review.

Purpose Of Review: Airway inflammation is a common biological response to many types of environmental exposures and can lead to increased nitric oxide (NO) concentrations in exhaled breath. In recent years, several studies have evaluated airway inflammation using fractional exhaled nitric oxide (FeNO) as a biomarker of exposures to a range of air pollutants. This systematic review aims to summarize the studies that collected personal-level air pollution data to assess the air pollution-induced FeNO responses and to determine if utilizing personal-level data resulted in an improved characterization of the relationship between air pollution exposures and FeNO compared to using only ambient air pollution exposure data.

Identifying optimal co-location calibration periods for low-cost sensors.

Low-cost sensors are often co-located with reference instruments to assess their performance and establish calibration equations, but limited discussion has focused on whether the duration of this calibration period can be optimized. We placed a multipollutant monitor that contained sensors that measure particulate matter smaller than 2.5 μm (PM), carbon monoxide (CO), nitrogen dioxide (NO), ozone (O), and nitric oxide (NO) at a reference field site for one year.

Laboratory and field evaluation of a low-cost methane sensor and key environmental factors for sensor calibration.

Low-cost sensors enable finer-scale spatiotemporal measurements within the existing methane (CH) monitoring infrastructure and could help cities mitigate CH emissions to meet their climate goals. While initial studies of low-cost CH sensors have shown potential for effective CH measurement at ambient concentrations, sensor deployment remains limited due to questions about interferences and calibration across environments and seasons. This study evaluates sensor performance across seasons with specific attention paid to the sensor's understudied carbon monoxide (CO) interferences and environmental dependencies through long-term ambient co-location in an urban environment.

Non-linear probabilistic calibration of low-cost environmental air pollution sensor networks for neighborhood level spatiotemporal exposure assessment.

Background: Low-cost sensor networks for monitoring air pollution are an effective tool for expanding spatial resolution beyond the capabilities of existing state and federal reference monitoring stations. However, low-cost sensor data commonly exhibit non-linear biases with respect to environmental conditions that cannot be captured by linear models, therefore requiring extensive lab calibration. Further, these calibration models traditionally produce point estimates or uniform variance predictions which limits their downstream in exposure assessment.

Evaluating the Performance of Using Low-Cost Sensors to Calibrate for Cross-Sensitivities in a Multipollutant Network.

As part of our low-cost sensor network, we colocated multipollutant monitors containing sensors for particulate matter, carbon monoxide, ozone, nitrogen dioxide, and nitrogen monoxide at a reference field site in Baltimore, MD, for 1 year. The first 6 months were used for training multiple regression models, and the second 6 months were used to evaluate the models. The models produced accurate hourly concentrations for all sensors except ozone, which likely requires nonlinear methods to capture peak summer concentrations.

Laboratory Chamber Evaluation of Flow Air Quality Sensor PM and PM Measurements.

The emergence of low-cost air quality sensors as viable tools for the monitoring of air quality at population and individual levels necessitates the evaluation of these instruments. The Flow air quality tracker, a product of Plume Labs, is one such sensor. To evaluate these sensors, we assessed 34 of them in a controlled laboratory setting by exposing them to PM and PM and compared the response with Plantower A003 measurements.

Maternal exposure to polycyclic aromatic hydrocarbons in South Texas, evaluation of silicone wristbands as personal passive samplers.

Background: Prenatal exposure to polycyclic aromatic hydrocarbons (PAHs) is associated with adverse health effects in children. Valid exposure assessment methods with accurate spatial and temporal resolution across pregnancy is a critical need for advancing environmental health studies.

Objective: The objective of this study was to quantify maternal PAH exposure in pregnant women residing in McAllen, Texas where the prematurity rate and childhood asthma prevalence rates are high.

Benzene Exposure and Cancer Risk from Commercial Gasoline Station Fueling Events Using a Novel Self-Sampling Protocol.

Tens of millions of individuals go to gasoline stations on a daily basis in the United States. One of the constituents of gasoline is benzene, a Group 1 carcinogen that has been strongly linked to both occupational and non-occupational leukemias. While benzene content in gasoline is federally regulated, there is approximately a thirty-year data gap in United States research on benzene exposures from pumping gasoline.

Stationary and portable multipollutant monitors for high-spatiotemporal-resolution air quality studies including online calibration.

The distribution and dynamics of atmospheric pollutants are spatiotemporally heterogeneous due to variability in emissions, transport, chemistry, and deposition. To understand these processes at high spatiotemporal resolution and their implications for air quality and personal exposure, we present custom, low-cost air quality monitors that measure concentrations of contaminants relevant to human health and climate, including gases (e.g.

Statistical field calibration of a low-cost PM monitoring network in Baltimore.

Low-cost air pollution monitors are increasingly being deployed to enrich knowledge about ambient air-pollution at high spatial and temporal resolutions. However, unlike regulatory-grade (FEM or FRM) instruments, universal quality standards for low-cost sensors are yet to be established and their data quality varies widely. This mandates thorough evaluation and calibration before any responsible use of such data.

One Year Evaluation of Three Low-Cost PM Monitors.

The availability of low-cost monitors marketed for use in homes has increased rapidly over the past few years due to the advancement of sensing technologies, increased awareness of urban pollution, and the rise of citizen science. The user-friendly packages can make them appealing for use in research grade indoor exposure assessments, but a rigorous scientific evaluation has not been conducted for many monitors on the open market, which leads to uncertainty about the validity of the data. Furthermore, many previous sensor studies were conducted for a relatively short period of time, which may not capture the changes this type of instrument may exhibit over time (known as sensor aging).

Field and Laboratory Evaluations of the Low-Cost Plantower Particulate Matter Sensor.

Due to the rapid development of low-cost air-quality sensors, a rigorous scientific evaluation has not been conducted for many available sensors. We evaluated three Plantower PMS A003 sensors when exposed to eight particulate matter (PM) sources (i.e.

Maternal exposure to PM in south Texas, a pilot study.

In this study, we characterized personal exposure to fine particulate matter (PM), black carbon (BC), and nicotine in pregnant women in Hidalgo County, where the hospitalization rates of childhood asthma are the highest in the state of Texas. The measurements were conducted over three non-consecutive sampling days for 17 participants in their third trimester. Measurements were partitioned into four microenvironments, i.

Persistent sulfate formation from London Fog to Chinese haze.

Sulfate aerosols exert profound impacts on human and ecosystem health, weather, and climate, but their formation mechanism remains uncertain. Atmospheric models consistently underpredict sulfate levels under diverse environmental conditions. From atmospheric measurements in two Chinese megacities and complementary laboratory experiments, we show that the aqueous oxidation of SO by NO is key to efficient sulfate formation but is only feasible under two atmospheric conditions: on fine aerosols with high relative humidity and NH neutralization or under cloud conditions.

Markedly enhanced absorption and direct radiative forcing of black carbon under polluted urban environments.

Black carbon (BC) exerts profound impacts on air quality and climate because of its high absorption cross-section over a broad range of electromagnetic spectra, but the current results on absorption enhancement of BC particles during atmospheric aging remain conflicting. Here, we quantified the aging and variation in the optical properties of BC particles under ambient conditions in Beijing, China, and Houston, United States, using a novel environmental chamber approach. BC aging exhibits two distinct stages, i.