About

Mei Tessum is a Research Assistant Professor of Agricultural & Biological Engineering leading the Tessum Research Lab, which focuses on assessing air pollution levels and their human health impacts. Her research emphasizes air pollution monitoring, environmental exposure modeling, and aerosol measurement and control technology. The lab develops air quality monitoring strategies to collect high-quality air pollution data and produces accurate air pollution exposure estimates for health studies in both ambient and occupational settings using advanced statistical and machine learning methods. Tessum's approach integrates air sampling, exposure modeling, and machine learning to improve understanding of pollutant distribution and human exposure over space and time. Her projects address critical issues such as agriculture-related ambient air pollution, which is a growing concern for air quality, public health, climate change, and environmental justice, and traffic-related air pollution prediction using video footage and deep learning to provide hyperlocal pollution quantification. This work aims to overcome limitations of traditional direct-measurement techniques that are costly and labor-intensive, thereby advancing environmental sustainability and social equity.

Research topics

• Chromatography
• Nanotechnology
• Geography
• Mechanics
• Environmental protection
• Electrical engineering
• Chemistry
• Physics
• Materials science
• Environmental science
• Thermodynamics
• Meteorology
• Environmental health

Selected publications

• Utility and Safety of Compressed Air in Preventing Grain Entrapment

  Journal of Agricultural Safety and Health · 2025-01-01

  article

  HIGHLIGHTS: Compressed air strategy was evaluated as a grain entrapment prevention method. Nozzle types affected compressed air efficiency. Open ½ inch nozzles performed best. Mid-scale experiment confirmed compressed air utility in breaking grain clumps. Dust, fungal, and noise levels exceeded maximum limits during operations, and PPE must be worn properly before using compressed air to break grain clumps. ABSTRACT: Grain entrapment, a severe and often fatal agricultural hazard, continues to pose a significant challenge in grain storage and handling. These incidents are often due to out-of-condition grain blocking outlets, leading to workers frequently entering the grain bin to dislodge grain. This study evaluates the utility of compressed air as a preventive measure to break up grain clumps located at bin outlets by conducting pilot and full-scale experiments using an air compressor. This study also evaluated potential hazards due to the use of air compressors. Three nozzle types were tested: open ½ inch, Crimped ½ inch, and the AirSpade. The findings indicated that the open ½ inch nozzle was the most efficient, with an average clearing time of 15 minutes per run, outperforming the crimped and AirSpade nozzles. Noise levels during operation ranged up to 105 dBA, with dust and fungal spore concentrations exceeding safety limits inside the grain bins and returning to acceptable levels shortly after operation. Full-scale testing indicates that compressed air can be useful in unclogging bins. The study underscores the potential of compressed air to enhance grain handling safety, offering practical safety recommendations and suggesting the need for further research to optimize and standardize its use in preventing grain entrapment.

  PublisherDOI

• Sources of ambient PM2.5 exposure in 96 global cities

  ChemRxiv · 2022-02-20 · 1 citations

  preprintOpen access1st authorCorresponding

  To improve air quality, knowledge of the sources and locations of air pollutant emissions is critical. However, for many global cities, no previous estimates exist of how much exposure to fine particulate matter (PM2.5), the largest environmental cause of mortality, is caused by emissions within the city vs. outside its boundaries. We use the Intervention Model for Air Pollution (InMAP) global-through-urban reduced complexity air quality model with a high-resolution, global inventory of pollutant emissions to quantify the contribution of emissions by source type and location for 96 global cities. Among these cities, we find that the fraction of PM2.5 exposure caused by within-city emissions varies widely (µ=51%; σ=23%) and is not well-explained by surrounding population density. The list of most-important sources also varies by city. Compared to a more mechanistically detailed model, InMAP predicts urban measured concentrations with less bias but more error. Predictive accuracy in urban areas is not particularly high with either model, suggesting an opportunity for improving global urban air emission inventories. We expect the results herein can be useful as a screening tool for policy options and in many cases may be robust enough to inform policy action to improve public health.

  PublisherOA PDFDOI

• Sources of ambient PM2.5 exposure in 96 global cities

  Atmospheric Environment · 2022 · 54 citations

  1st authorCorresponding
  • Environmental science
  • Geography
  • Environmental protection

  exposure caused by within-city emissions varies widely (μ = 37%; σ = 22%) and is not well-explained by surrounding population density. The list of most-important sources also varies by city. Compared to a more mechanistically detailed model, InMAP predicts urban measured concentrations with lower bias and error but also lower correlation. Predictive accuracy in urban areas is not particularly high with either model, suggesting an opportunity for improving global urban air emission inventories. We expect the results herein can be useful as a screening tool for policy options and, in the absence of available resources for further analysis, to inform policy action to improve public health.

  PublisherDOI

• Improving Air Pollution Predictions of Long-Term Exposure Using Short-Term Mobile and Stationary Monitoring in Two US Metropolitan Regions

  Environmental Science & Technology · 2021-02-26 · 9 citations

  articleOpen access1st authorCorresponding

  Mobile monitoring is increasingly employed to measure fine spatial-scale variation in air pollutant concentrations. However, mobile measurement campaigns are typically conducted over periods much shorter than the decadal periods used for modeling chronic exposure for use in air pollution epidemiology. Using the regions of Los Angeles and Baltimore and the time period from 2005 to 2014 as our modeling domain, we investigate whether including mobile or stationary passive sampling device (PSD) monitoring data collected over a single 2-week period in one or two seasons using a unified spatio-temporal air pollution model can improve model performance in predicting NO2 and NOx concentrations throughout the 9-year study period beyond what is possible using only routine monitoring data. In this initial study, we use data from mobile measurement campaigns conducted contemporaneously with deployments of stationary PSDs and only use mobile data collected within 300 m of a stationary PSD location for inclusion in the model. We find that including either mobile or PSD data substantially improves model performance for pollutants and locations where model performance was initially the worst (with the most-improved R2 changing from 0.40 to 0.82) but does not meaningfully change performance in cases where performance was already very good. Results indicate that in many cases, additional spatial information from mobile monitoring and personal sampling is potentially cost-efficient inexpensive way of improving exposure predictions at both 2-week and decadal averaging periods, especially for the predictions that are located closer to features such as roadways targeted by the mobile short-term monitoring campaign.

  PublisherOA PDFDOI

• Measuring electrostatic charge on pneumatically generated spray drops

  Journal of Aerosol Science · 2020 · 12 citations

  1st authorCorresponding
  • Chemistry
  • Materials science
  • Mechanics
  PublisherDOI

• spatialmodel/inmap: v1.5.1

  Zenodo (CERN European Organization for Nuclear Research) · 2019-01-26

  articleOpen accessSenior author

  <strong>Documentation for this release is available here.</strong> This release contains: The InMAP model as described here The Extended InMAP Economic Input-Output (EIEIO) model, created by CW Tessum, JS Apte, AL Goodkind, NZ Muller, KA Mullins, DA Paolella, S Polasky, NP Springer, SK Thakrar, JD Marshall, and JD Hill The GREET-cst model, created by CW Tessum, JD Marshall, and JD Hill; a previous version is described here Data for evaluating and running the model is the same as in v1.3 and can be downloaded from the following link: Changes in version 1.5.1: Update go.mod dependencies

  PublisherDOI

• Mobile and Fixed-Site Measurements To Identify Spatial Distributions of Traffic-Related Pollution Sources in Los Angeles

  Environmental Science & Technology · 2018-01-31 · 47 citations

  articleOpen access1st authorCorresponding

  Mobile monitoring and fixed-site monitoring using passive sampling devices (PSD) are popular air pollutant measurement techniques with complementary strengths and weaknesses. This study investigates the utility of combining data from concurrent 2-week mobile monitoring and fixed-site PSD campaigns in Los Angeles in the summer and early spring to identify sources of traffic-related air pollutants (TRAP) and their spatial distributions. There were strong to moderate correlations between mobile and fixed-site PSD measurements of both NO2 and NOx in the summer and spring (Pearson’s r between 0.43 and 0.79), suggesting that the two data sets can be reliably combined for source apportionment. PCA identified the major TRAP sources as light-duty vehicle emissions, diesel exhaust, crankcase vent emissions, and an independent source of combustion-derived ultrafine particle emissions. The component scores of those four sources at each site were significantly correlated across the two seasons (Pearson’s r between 0.58 and 0.79). Spatial maps of absolute principal component scores showed all sources to be most prominent near major roadways and the central business district and the ultrafine particle source being, in addition, more prominent near the airport. Mobile monitoring combined with fixed-site PSD sampling can provide high spatial resolution estimates of TRAP and can reveal underlying sources of exposure variability.

  PublisherOA PDFDOI

• Incorporating Mobile Monitoring Data in Spatio-Temporal Air Pollution Modeling

  ISEE Conference Abstracts · 2018-09-24

  article1st authorCorresponding

  Air pollution measurements from mobile platforms could potentially increase the accuracy of pollutant exposure prediction models. However, it can be challenging to separate spatial and temporal variability when including mobile measurements in exposure prediction models. This study investigates whether using mobile monitoring data in a spatio-temporal air pollution model can improve the performance of an exposure model in predicting NO2 and NOx concentrations compared to a model created using only routine monitoring data. Three model scenarios were tested using a unified spatiotemporal modeling approach for the Los Angeles region from 2005 &amp;#8211; 2014: 1) a model using two-week averaged AQS and the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) fixed site data only, 2) a model using those AQS and MESA Air fixed site data plus two-week averaged mobile monitoring data, and 3) a model using AQS and MESA Air fixed site data plus two-week averaged passive sampler data (concurrent and collocated with mobile monitoring). Additional measurements from MESA Air home sites were used for model validation. Models with either mobile monitoring data or passive sampler data improved model performance in home site predictions for both NOx and NO2 compared to models developed from routine measurements only. Mmodels using passive sampler data performed better than models created using mobile monitoring data. Results indicate that additional spatial information from mobile monitoring data can improve the spatio-temporal model performance, but passive sampler measurements may be preferable if available.

  PublisherDOI

• Effects of Spray Surfactant and Particle Charge on Respirable Coal Dust Capture

  Safety and Health at Work · 2017-02-06 · 90 citations

  articleOpen access1st authorCorresponding

  BACKGROUND: Surfactant-containing water sprays are commonly used in coal mines to collect dust. This study investigates the dust collection performance of different surfactant types for a range of coal dust particle sizes and charges. METHODS: Bituminous coal dust aerosol was generated in a wind tunnel. The charge of the aerosol was either left unaltered, charge-neutralized with a neutralizer, or positively- or negatively-charged using a diffusion charger after the particles were neutralized. An anionic, cationic, or nonionic surfactant spray or a plain water spray was used to remove the particles from the air flow. Some particles were captured while passing through spray section, whereas remaining particles were charge-separated using an electrostatic classifier. Particle size and concentration of the charge-separated particles were measured using an aerodynamic particle sizer. Measurements were made with the spray on and off to calculate overall collection efficiencies (integrated across all charge levels) and efficiencies of particles with specific charge levels. RESULTS: The diameter of the tested coal dust aerosol was 0.89 μm ± 1.45 [geometric mean ± geometric standard deviations (SD)]. Respirable particle mass was collected with 75.5 ± 5.9% (mean ± SD) efficiency overall. Collection efficiency was correlated with particle size. Surfactant type significantly impacted collection efficiency: charged particle collection by nonionic surfactant sprays was greater than or equal to collection by other sprays, especially for weakly-charged aerosols. Particle charge strength was significantly correlated with collection efficiency. CONCLUSION: Surfactant type affects charged particle spray collection efficiency. Nonionic surfactant sprays performed well in coal dust capture in many of the tested conditions.

  PublisherDOI

• Effects of Spray Surfactant and Particle Charge on Respirable Dust Control

  University of Minnesota Digital Conservancy (University of Minnesota) · 2015-06-01

  dissertationOpen access1st authorCorresponding

  University of Minnesota Ph.D. dissertation. 2015. Major: Environmental Health. Advisor: Peter Raynor. 1 computer file (PDF); 168 pages.

  Publisher

Frequent coauthors

• Sverre Vedal

  University of Washington

  4 shared

• Peter C. Raynor

  University of Minnesota

  4 shared

• Christopher W. Tessum

  University of Illinois Urbana-Champaign

  3 shared

• Timothy Gould

  University of Washington

  3 shared

• Timothy V. Larson

  University of Washington

  3 shared

• Daven K. Henze

  University of Colorado Boulder

  3 shared

• Lianne Sheppard

  University of Washington

  2 shared

• Iyad Kheirbek

  Climate Central

  2 shared

Labs

• Tessum Research LabPI

  Assesses air pollution levels and human health impacts, focusing on air pollution monitoring, environmental exposure modeling, and aerosol measurement and control technology.