Tropospheric sounding with low-cost particulate matter sensors

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

The Tropical Forest and Fire Emissions Experiment: Emission, Chemistry, and Transport of Biogenic Volatile Organic Compounds in the Lower Atmosphere Over Amazonia

Airborne and ground-based mixing ratio and flux measurements using eddy covariance (EC) and for the first time the mixed layer gradient (MLG) and mixed layer variance (MLV) techniques are used to assess the impact of isoprene and monoterpene emissions on atmospheric chemistry in the Amazon basin. Average noon isoprene (7.8 +/- 2.3 mg/m(2)/h) and monoterpene fluxes (1.2 +/- 0.5 mg/m(2)/h)compared well between ground and airborne measurements and are higher than fluxes estimated in this region during other seasons. The biogenic emission model, Model of Emissions of Gases and Aerosols from Nature (MEGAN), estimates fluxes that are within the model and measurement uncertainty and can describe the large observed variations associated with land-use change in the region north-west of Manaus. Isoprene and monoterpenes accounted for similar to 75% of the total OH reactivity in this region and are important volatile organic compounds (VOCs) for modeling atmospheric chemistry in Amazonia. The presence of fair weather clouds ( cumulus humilis) had an important impact on the vertical distribution and chemistry of VOCs through the planetary boundary layer (PBL), the cloud layer, and the free troposphere (FT). Entrainment velocities between 10: 00 and 11: 30 local time ( LT) are calculated to be on the order of 8-10 cm/s. The ratio of methyl-vinyl-ketone (MVK) and methacrolein (MAC) ( unique oxidation products of isoprene chemistry) with respect to isoprene showed a pronounced increase in the cloud layer due to entrainment and an increased oxidative capacity in broken cloud decks. A decrease of the ratio in the lower free troposphere suggests cloud venting through activated clouds. OH modeled in the planetary boundary layer using a photochemical box model is much lower than OH calculated from a mixed layer budget approach. An ambient reactive sesquiterpene mixing ratio of 1% of isoprene would be sufficient to explain most of this discrepancy. Increased OH production due to increased photolysis in the cloud layer balances the low OH values modeled for the planetary boundary layer. The intensity of segregation ( Is) of isoprene and OH, defined as a relative reduction of the reaction rate constant due to incomplete mixing, is found to be significant: up to 39 +/- 7% in the similar to 800-m-deep cloud layer. The effective reaction rate between isoprene and OH can therefore vary significantly in certain parts of the lower atmosphere

A new paradigm of quantifying ecosystem stress through chemical signatures

Stress-induced emissions of biogenic volatile organic compounds (VOCs) from terrestrial eco- systems may be one of the dominant sources of VOC emissions worldwide. Understanding the ecosystem stress response could reveal how ecosystems will respond and adapt to climate change and, in turn, quan- tify changes in the atmospheric burden of VOC oxidants and secondary organic aerosols. Here, we argue, based on preliminary evidence from several opportunistic measurement sources, that chemical signatures of stress can be identified and quantified at the ecosystem scale. We also outline future endeavors that we see as next steps toward uncovering quantitative signatures of stress, including new advances in both VOC data collection and analysis of "big data.

Satellite-derived Constraints on the Effect of Drought Stress on Biogenic Isoprene Emissions in the Southeast US

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) &ndash; 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&ndash;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 (&gamma;d_OMI) in GEOS-Chem that parameterizes using water stress and temperature. The algorithm led to an 8.6 % (mild drought) &ndash; 20.7 % (severe drought) reduction in isoprene emissions in the SE US relative to the simulation without it. With &gamma;d_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&rsquo;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&rsquo;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 &lt;1 ppb increase in O3 in high-isoprene regions and a 1&ndash;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.</p

Processing arctic eddy-flux data using a simple carbon-exchange model embedded in the ensemble Kalman filter

Author Posting. © Ecological Society of America, 2010. This article is posted here by permission of Ecological Society of America for personal use, not for redistribution. The definitive version was published in Ecological Applications 20 (2010): 1285–1301, doi:10.1890/09-0876.1.Continuous time-series estimates of net ecosystem carbon exchange (NEE) are routinely made using eddy covariance techniques. Identifying and compensating for errors in the NEE time series can be automated using a signal processing filter like the ensemble Kalman filter (EnKF). The EnKF compares each measurement in the time series to a model prediction and updates the NEE estimate by weighting the measurement and model prediction relative to a specified measurement error estimate and an estimate of the model-prediction error that is continuously updated based on model predictions of earlier measurements in the time series. Because of the covariance among model variables, the EnKF can also update estimates of variables for which there is no direct measurement. The resulting estimates evolve through time, enabling the EnKF to be used to estimate dynamic variables like changes in leaf phenology. The evolving estimates can also serve as a means to test the embedded model and reconcile persistent deviations between observations and model predictions. We embedded a simple arctic NEE model into the EnKF and filtered data from an eddy covariance tower located in tussock tundra on the northern foothills of the Brooks Range in northern Alaska, USA. The model predicts NEE based only on leaf area, irradiance, and temperature and has been well corroborated for all the major vegetation types in the Low Arctic using chamber-based data. This is the first application of the model to eddy covariance data. We modified the EnKF by adding an adaptive noise estimator that provides a feedback between persistent model data deviations and the noise added to the ensemble of Monte Carlo simulations in the EnKF. We also ran the EnKF with both a specified leaf-area trajectory and with the EnKF sequentially recalibrating leaf-area estimates to compensate for persistent model-data deviations. When used together, adaptive noise estimation and sequential recalibration substantially improved filter performance, but it did not improve performance when used individually. The EnKF estimates of leaf area followed the expected springtime canopy phenology. However, there were also diel fluctuations in the leaf-area estimates; these are a clear indication of a model deficiency possibly related to vapor pressure effects on canopy conductance.This material is based upon work supported by the U.S. National Science Foundation under grants OPP-0352897, DEB-0423385, DEB-0439620, DEB-0444592, and OPP- 0632139

Low-cost HAB platform to measure particulate matter in the troposphere

High-altitude balloons (HABs) are an engaging platform for formal and informal STEM education. However, the logistics of launching, chasing and recovering a payload on a 1200 g or 1500 g balloon can be daunting for many novice school groups and citizen scientists, and the cost can be prohibitive. In addition, there are many interesting scientific applications that do not require reaching the stratosphere. In this poster presentation we discuss a novel approach based on small (30 g) balloons that are cheap and easy to handle, and low-cost tracking devices (SPOT and 900 MHz spread spectrum) that do not require a license. Our scientific goal is to measure air quality in the lower troposphere. Particulate matter (PM) is an air pollutant that varies on small spatial scales and has sources in rural areas like biomass burning and farming practices such as tilling. Our HAB platform incorporates an optical PM sensor, an integrated single board computer that records the PM sensor signal in addition to flight parameters (pressure, location and altitude), and a low-cost tracking system. Our goal is for the entire platform to cost less than $500

Low-cost HAB platform to measure particulate matter in the troposphere

High-altitude balloons (HABs) are an engaging platform for formal and informal STEM education. However, the logistics of launching, chasing and recovering a payload on a 1200 g or 1500 g balloon can be daunting for many novice school groups and citizen scientists, and the cost can be prohibitive. In addition, there are many interesting scientific applications that do not require reaching the stratosphere. In this poster presentation we discuss a novel approach based on small (30 g) balloons that are cheap and easy to handle, and low-cost tracking devices (SPOT and 900 MHz spread spectrum) that do not require a license. Our scientific goal is to measure air quality in the lower troposphere. Particulate matter (PM) is an air pollutant that varies on small spatial scales and has sources in rural areas like biomass burning and farming practices such as tilling. Our HAB platform incorporates an optical PM sensor, an integrated single board computer that records the PM sensor signal in addition to flight parameters (pressure, location and altitude), and a low-cost tracking system. Our goal is for the entire platform to cost less than $500

Monitoring Particulate Matter with Wearable Sensors and the Influence on Student Environmental Attitudes

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

Tropospheric sounding with low-cost particulate matter sensors

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