Mark J. Potosnak

Prescribed burns are a land management tool currently used to aid in fire mitigation and to promote desired plant species and reduce undesired species, which are often invasive species. Currently, there is a public stigma surrounding the negative effects of prescribed burns, which impacts their use near residential areas. There are also particular challenges of conducting prescribed burns in urban and suburban areas. A better understanding of these relatively small-scale prescribed burns and their impact on atmospheric chemistry and air quality can allow for better communication with the public about their positive impacts, as well as for the acknowledgement and quantification of their drawbacks. This study considers the particulate matter concentrations proximate to prescribed fires being conducted in the greater Chicago metropolitan area. We deployed low-cost sensors in the spring and fall of 2022 at four sites. Concentrations of particulate matter exceeded accepted human health exposure limits at locations that burn crews would experience, but levels quickly returned to baseline after the high-intensity phase of the burn. With the ever-expanding wildland–urban interface and increased efforts in ecological restoration, understanding the particulate matter emissions of prescribed fires conducted near populated areas can provide more data regarding air quality impacts. These impacts should be weighed against the ecological benefits of prescribed burns and potential air quality impacts from uncontrolled burns from ecosystems that are not treated with prescribed burns. Measuring air quality impacts can also inform fire management practices with the goal of reducing future emissions. With the very present effects of climate change, understanding baselines regarding prescribed fires can better equip future research and fire professionals in fighting fire with fire.

While substantial progress has been made to improve our understanding of biogenic isoprene emissions under unstressed conditions, large uncertainties remain with respect to isoprene emissions under stressed conditions. Here, we use the US Drought Monitor (USDM) as a weekly drought severity index and tropospheric columns of formaldehyde (HCHO), the key product of isoprene oxidation, retrieved from the Ozone Monitoring Instrument (OMI) to derive top-down constraints on the response of summertime isoprene emissions to drought stress in the southeastern United States (SE US), a region of high isoprene emissions that is also prone to drought. OMI HCHO column density is found to be 6.7 % (mild drought) to 23.3 % (severe drought) higher than that under non-drought conditions. A global chemical transport model, GEOS-Chem, with version 2.1 of the Model of Emissions of Gases and Aerosols from Nature (MEGAN2.1) emission algorithm can simulate this direction of change, but the simulated increases at the corresponding drought levels are 1.1–1.5 times that of OMI HCHO, suggesting the need for a drought-stress algorithm in the model. By minimizing the model–OMI differences in HCHO to temperature sensitivity under different drought levels, we derived a top-down drought stress factor (γ_{d_OMI}) in GEOS-Chem that parameterizes using water stress and temperature. The algorithm led to an 8.6 % (mild drought) to 20.7 % (severe drought) reduction in isoprene emissions in the SE US relative to the simulation without it. With γ_{d_OMI} the model predicts a nonlinear increasing trend in isoprene emissions with drought severity that is consistent with OMI HCHO and a single site's isoprene flux measurements. Compared with a previous drought stress algorithm derived from the latter, the satellite-based drought stress factor performs better with respect to capturing the regional-scale drought–isoprene responses, as indicated by the near-zero mean bias between OMI and simulated HCHO columns under different drought conditions. The drought stress algorithm also reduces the model's high bias in organic aerosol (OA) simulations by 6.60 % (mild drought) to 11.71 % (severe drought) over the SE US compared to the no-stress simulation. The simulated ozone response to the drought stress factor displays a spatial disparity due to the isoprene-suppressing effect on oxidants, with an <1 ppb increase in O₃ in high-isoprene regions and a 1–3 ppbv decrease in O₃ in low-isoprene regions. This study demonstrates the unique value of exploiting long-term satellite observations to develop empirical stress algorithms on biogenic emissions where in situ flux measurements are limited.

While substantial progress has been made to improve our understanding of biogenic isoprene emissions under unstressed conditions, there remain large uncertainties in isoprene emissions under stressed conditions. Here we use the US Drought Monitor (USDM) as a weekly drought severity index and tropospheric columns of formaldehyde (HCHO), the key product of isoprene oxidation, retrieved from the Ozone Monitoring Instrument (OMI) to derive top-down constraints on the response of summertime isoprene emissions to drought stress in the Southeast U.S. (SE US), a region of high isoprene emissions and prone to drought. OMI HCHO column density is found to be 5.3 % (mild drought) – 19.8 % (severe drought) higher than that in no-drought conditions. A global chemical transport model, GEOS-Chem, with the MEGAN2.1 emission algorithm can simulate this direction of change, but the simulated increases at the corresponding drought levels are 1.4–2.0 times of OMI HCHO, suggesting the need for a drought-stress algorithm in the model. By minimizing the model-to-OMI differences in HCHO to temperature sensitivity under different drought levels, we derived a top-down drought stress factor (_{γd_OMI}) in GEOS-Chem that parameterizes using water stress and temperature. The algorithm led to an 8.6 % (mild drought) – 20.7 % (severe drought) reduction in isoprene emissions in the SE US relative to the simulation without it. With _{γd_OMI} the model predicts a non-uniform trend of increase in isoprene emissions with drought severity that is consistent with OMI HCHO and a single site’s isoprene flux measurements. Compared with a previous drought stress algorithm derived from the latter, the satellite-based drought stress factor performs better in capturing the regional scale drought-isoprene responses as indicated by the close-to-zero mean bias between OMI and simulated HCHO columns under different drought conditions. The drought stress algorithm also reduces the model’s high bias in organic aerosols (OA) simulations by 6.60 % (mild drought) to 11.71 % (severe drought) over the SE US compared to the no-stress simulation. The simulated ozone response to the drought stress factor displays a spatial disparity due to the isoprene suppressing effect on oxidants, with an 3 in high-isoprene regions and a 1–3 ppbv decrease in O₃ in low-isoprene regions. This study demonstrates the unique value of exploiting long-term satellite observations to develop empirical stress algorithms on biogenic emissions where in situ flux measurements are limited.

While substantial progress has been made to improve our understanding of biogenic isoprene emissions under unstressed conditions, there remain large uncertainties in isoprene emissions under stressed conditions. Here we use the US Drought Monitor (USDM) as a weekly drought severity index and tropospheric columns of formaldehyde (HCHO), the key product of isoprene oxidation, retrieved from the Ozone Monitoring Instrument (OMI) to derive top-down constraints on the response of summertime isoprene emissions to drought stress in the Southeast U.S. (SE US), a region of high isoprene emissions and prone to drought. OMI HCHO column density is found to be 5.3 % (mild drought) – 19.8 % (severe drought) higher than that in no-drought conditions. A global chemical transport model, GEOS-Chem, with the MEGAN2.1 emission algorithm can simulate this direction of change, but the simulated increases at the corresponding drought levels are 1.4–2.0 times of OMI HCHO, suggesting the need for a drought-stress algorithm in the model. By minimizing the model-to-OMI differences in HCHO to temperature sensitivity under different drought levels, we derived a top-down drought stress factor (_{γd_OMI}) in GEOS-Chem that parameterizes using water stress and temperature. The algorithm led to an 8.6 % (mild drought) – 20.7 % (severe drought) reduction in isoprene emissions in the SE US relative to the simulation without it. With _{γd_OMI} the model predicts a non-uniform trend of increase in isoprene emissions with drought severity that is consistent with OMI HCHO and a single site’s isoprene flux measurements. Compared with a previous drought stress algorithm derived from the latter, the satellite-based drought stress factor performs better in capturing the regional scale drought-isoprene responses as indicated by the close-to-zero mean bias between OMI and simulated HCHO columns under different drought conditions. The drought stress algorithm also reduces the model’s high bias in organic aerosols (OA) simulations by 6.60 % (mild drought) to 11.71 % (severe drought) over the SE US compared to the no-stress simulation. The simulated ozone response to the drought stress factor displays a spatial disparity due to the isoprene suppressing effect on oxidants, with an 3 in high-isoprene regions and a 1–3 ppbv decrease in O₃ in low-isoprene regions. This study demonstrates the unique value of exploiting long-term satellite observations to develop empirical stress algorithms on biogenic emissions where in situ flux measurements are limited.

Biogenic volatile organic compounds (BVOCs), primarily emitted by terrestrial vegetation, are highly reactive and have large effects on the oxidizing potential of the troposphere, air quality and climate. In terms of global emissions, isoprene is the most important BVOC. Droughts bring about changes in the surface emission of biogenic hydrocarbons mainly because plants suffer water stress. Past studies report that the current parameterization in the state-of-the-art Model of Emissions of Gases and Aerosols from Nature (MEGAN) v2.1, which is a function of the soil water content and the permanent wilting point, fails at representing the strong reduction in isoprene emissions observed in field measurements conducted during a severe drought. Since the current algorithm was originally developed based on potted plants, in this study, we update the parameterization in the light of recent ecosystem-scale measurements of isoprene conducted during natural droughts in the central U.S. at the Missouri Ozarks AmeriFlux (MOFLUX) site. The updated parameterization results in stronger reductions in isoprene emissions. Evaluation using satellite formaldehyde (HCHO), a proxy for BVOC emissions, and a chemical-transport model, shows that the adjusted parameterization provides a better agreement between the modelled and observed HCHO temporal variability at local and regional scales in 2011–2012, even if it worsens the model agreement in a global, long-term evaluation. We discuss the limitations of the current parameterization, a function of highly uncertain soil properties such as porosity.

The mobile monitoring of air pollution is a growing field, prospectively filling in spatial gaps while personalizing air-quality-based risk assessment. We developed wearable sensors to record particulate matter (PM), and through a community science approach, students of partnering Chicago high schools monitored PM concentrations during their commutes over a five- and thirteen-day period. Our main objective was to investigate how mobile monitoring influenced students’ environmental attitudes and we did this by having the students explore the relationship between PM concentrations and urban vegetation. Urban vegetation was approximated with a normalized difference vegetation index (NDVI) using Landsat 8 satellite imagery. While the linear regression for one partner school indicated a negative correlation between PM and vegetation, the other indicated a positive correlation, contrary to our expectations. Survey responses were scored on the basis of their environmental affinity and knowledge. There were no significant differences between cumulative pre- and post-experiment survey responses at Josephinum Academy, and only one weakly significant difference in survey results at DePaul Prep in the Knowledge category. However, changes within certain attitudinal subscales may possibly suggest that students were inclined to practice more sustainable behaviors, but perhaps lacked the resources to do so.

The high-altitude balloon (HAB) platform has allowed scientists to measure vertical profiles in the atmosphere at a relatively low cost. The current project combines the HAB platform with low-cost air quality sensors that measure particulate matter (PM). PM is detrimental to human health and can exacerbate asthma. In the atmosphere, PM can affect cloud formation and also radiative transfer, which links emissions of PM to climate change. Therefore, understanding and controlling PM emissions is vital to air quality and climate change. In agricultural regions, several practices produce significant PM emissions. Tilling can release PM in the form of dust, especially under arid conditions. The burning of crop residue is also a common practice practice that releases PM in the form of partially combusted organics (soot). The ultimate goal of this project is to use low-cost PM sensors and HAB to assess PM sources from agricultural regions using citizen scientists. The current presentation evaluates the performance of two different PM sensors over flights conducted during the summer of 2017.

Urban trees provide numerous benefits, such as cooling from transpiration, carbon sequestration, and street aesthetics. But volatile organic compound emissions from trees can combine with anthropogenic nitrogen oxide emissions to form ozone, a harmful air pollutant. The most commonly-emitted of these compounds, isoprene, negatively impacts air quality and hence is detrimental to human health. In addition to environmental controls such as light and temperature, the quantity of isoprene emitted from a leaf is a genus-specific trait. Leaf isoprene emission is enzymatically controlled, and species are typically classified as emitters or non-emitters (near-zero emission rates). Therefore, the species composition of urban forests affects whole-system isoprene production. The process of plant invasion alters species composition, and invasive tree species can be either emitters or non-emitters. If an invasive, isoprene-emitting tree species displaces native, non-emitting species, then isoprene emission rates from urban forests will increase, with a concomitant deterioration of air quality. We tested a hypothesis that invasive species have higher isoprene emission rates than native species. Using existing tree species inventory data for the Chicago region, leaf-level isoprene emission rates of the six most common invasive and native tree species were measured and compared. The difference was not statistically significant, but this could be due to the variability associated with making a sufficient number of measurements to quantify species isoprene emission rates. The most common invasive species European buckthorn (Rhamnus cathartica, L.) was an emitter. Because European buckthorn often invades the disturbed edges common in urban forests, we tested a second hypothesis that edge-effect isoprene emissions would significantly increase whole-system modeled isoprene emissions. Using Google Earth satellite imagery to estimate forested area and edge length in the LaBagh Woods Forest Preserve of Cook County (Chicago, IL, USA), edge isoprene emission contributed 8.1% compared to conventionally modeled forest emissions. Our results show that the invasion of European buckthorn has increased isoprene emissions from urban forests. This implies that ecological restoration efforts to remove European buckthorn have the additional benefit of improving air quality.

The “Array of Things” project in Chicago, IL (U.S) is a ‘smart-cities’ joint initiative funded by the National Science Foundation and involving a partnership between Argonne National Laboratory, the University of Chicago, and the City of Chicago [1]. Several hundred of these platforms are distributed about the city of Chicago and each incorporates seven low-power electrochemical gas sensors for measuring the air quality health of the city. This collection of nodes is the foundation of an Industrial-Internet-of-Things, managed by a public institution, for the benefit of the surrounding communities. It presents a unique opportunity to develop smart sensor algorithms to calibrate these low-cost sensors, thereby providing meaningful and accurate data to map and identify sources of air pollution or to inform the communities of poor air quality days. To create the numerical calibration methods, ozone (O3), nitrogen dioxide (NO2), and carbon monoxide (CO) gas sensors are compared to reference grade monitors, with data collected between March 2018 and June 2019. The gas sensor sub-assembly board was calibrated in the labs at KWJ Engineering, Inc. (Newark, CA) in October 2017. The AoT node was installed and began operating at the EPA ComEd measurement site in Chicago (7801 S Lawndale Ave, Chicago, IL 60652), with help from Pinaki Banerjee of the Cook County Department of Environment and Sustainability. CO reference instrumentation was provided by Max Berkerlhamer of the University of Illinois – Chicago; NO2 and O3 data were from the EPA instruments run by Cook County. Sensor data is collected at 5-minute intervals, while reference data is reported at 1 hour (NO2/O3) and ½ hour intervals (CO). Over the course of 15 months, the reference data showed peak NO2 and O3 values of about 90 ppb, typically with opposite diurnal cycles. During the four months of CO reference monitoring, the range of recorded values were between 150 and 1,500 ppb. To reach these lower detection limits with low cost sensors, methods to remotely zero and span must be developed to overcome the effects of time, as well as outdoor temperature, humidity, and pressure extremes. The main advantage to EPA or research grade instrumentation is the ability to zero and calibrate the equipment at regular intervals. For instance, an EPA site may report an 8-hour moving average for O3 and/or NO2, and this equipment may be zeroed and calibrated on a weekly or daily schedule. For an air monitor mounted on a telephone pole, or internal to a publicly accessible structure (such as a bus stop), calibration methods requiring gas exposure are not feasible. As an alternative method, numerical zeroing using known or anticipated low values can enable an estimation of a zero. These low points can be timed to a weekly or sometimes a diurnal schedule. EC sensors have a typical baseline exponential sensitivity to temperature, which can change over time. Methods to predict this change for the CO sensor increased the R² correlation from 0.14 when using a static calibration factor to 0.72 when using a time dependent dynamic factor. For determining the appropriate numerical span values ‘nearest neighbor’ methods can be implemented, using either a publicly available reference station, such as an EPA monitoring site, or similarly timing to a known or expected high point. Also important to the sensor stability are averaging methods that can respond quickly to environmental releases of pollutants. Approaches to these methods depend on the targeted gas as well as the sensors' chemistry including the effects of interference from other gasses typically present in air. For example, in this system the NO2 and O3 co-dependence is also shown to change with time. For the NO2 sensor the baseline (diurnal minimum) should decay over time, similar to the CO sensor. However, with increasing sensitivity to O3, the baseline will start to increase, and this pattern can be used to infer the increasing sensitivity to O3. While improvements to sensor design will mitigate some of the shortcomings of low power sensors, this work highlights important numerical methods, and will provide examples to improve the integrity of low-power sensing nodes for future distributed gas sensing networks, allowing for wider implementation of smart-city platforms at a lower cost to the public for implementation. [1] Catlett CE, Beckman PH, Sankaran R, Galvin KK. Array of things: a scientific research instrument in the public way: platform design and early lessons learned, Proceedings of the 2nd International Workshop on Science of Smart City Operations and Platforms Engineering, 26-33 (2017) Figure 1