About

Zhen Qu is an assistant professor in the Department of Marine, Earth and Atmospheric Sciences at NC State University. She is an atmospheric chemist whose research focuses on understanding how human activities are altering atmospheric composition and how these changes interact with climate change. Her work utilizes statistics, high-performance computing, and satellite observations to investigate the sources, chemical formation, spatial distributions, and trends of air pollutants and greenhouse gases. She develops algorithms and modeling approaches to expand process-level understanding of man-made and natural emissions, and applies atmospheric chemistry models to assess the sensitivity of air pollution and human exposure to emissions of precursor gases. Zhen Qu teaches courses including Introduction to Atmospheric Chemistry and Atmospheric Physics, and she is actively seeking motivated students to join her research group.

Research topics

• Environmental science
• Meteorology
• Geography
• Remote sensing
• Geology
• Climatology

Selected publications

• Intercomparison of global ground-level ozone datasets for health-relevant metrics

  2025-01-03 · 1 citations

  preprintOpen accessCorresponding

  Abstract. Ground-level ozone is a significant air pollutant that detrimentally affects human health and agriculture. Global ground-level ozone concentrations have been estimated using chemical reanalyses, geostatistical methods, and machine learning, but these datasets have not been compared systematically. We compare six global ground-level ozone datasets (three chemical reanalyses, two machine learning, one geostatistics) against one another and relative to observations, for the ozone season daily maximum 8-hour average mixing ratio, for 2006 to 2016. Results show significant differences among datasets in global average ozone, as large as 5–10 ppb, multi-year trends, and regional distributions. For example, in Europe, the three chemical reanalyses show an increasing trend while the other datasets show no increase. Among the six datasets, the population exposed to over 50 ppb varies from 60.8 % to 99 % in East Asia, 17 % to 88 % in North America, and 9 % to 77 % in Europe (2006–2016 average). These differences are large enough to impact health and other applications. Comparing with Tropospheric Ozone Assessment Report (TOAR) II ground-level observations, most datasets overestimate ozone, particularly at lower observed concentrations. In 2016, across all stations, R2 ranges among the six datasets from 0.35 to 0.63, and RMSE from 5.28 to 13.49 ppb. Performance further declines when considering only stations with observations above 50 ppb. Although some datasets share some of the same input data, we found important differences among these datasets, likely from variations in approaches, resolution, and other input data, highlighting the importance of continued research on global ozone distributions.

  PublisherDOI

• Analysis of Hydrological Drought and Influencing Factors in Yunnan Province Using Integrated Gnss Observation Data and Xgboost Model

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Supplementary material to "Intercomparison of global ground-level ozone datasets for health-relevant metrics"

  2025-01-03 · 1 citations

  preprintOpen access

  The objective is to ascertain the grouping combination that maximizes the difference between and .For example, consider a grouping scenario where: Group A: BME, NJML, UKML Group B: CAMS, GEOS, TCR-2."pwcorr" denotes the operation to compute the pairwise correlation between two datasets.The calculations proceed as follows: Ci = pwcorr(BME, NJML)+ pwcorr(BME, UKML)+ pwcorr(NJML, UKML)+ pwcorr(CAMS, GEOS)+ pwcorr(CAMS, TCR-2)+ pwcorr(TCR-2, GEOS) Co = pwcorr(BME, GEOS)+ pwcorr(BME, TCR-2)+ pwcorr(BME, CAMS)+ pwcorr(NJML, GEOS)+ pwcorr(NJML, TCR-2)+ pwcorr(NJML, CAMS)+ pwcorr(UKML, GEOS)+ pwcorr(UKML, TCR-2)+ pwcorr(UKML, CAMS)This analysis is conducted for all 203 combinations, identifying the optimal grouping that maximizes the difference.Group A: BME, UKML, CAMS, GEOS, TCR-2 Group B: NJML We also use the hierarchical clustering method to group 6 datasets based on pairwise correlation (R) and get the same result.The hierarchical clustering is typically used with a dissimilarity (distance) matrix, where the elements represent the distances or dissimilarities between data (Bishop, 2006).2003-2019 80 km/0.75 CAMS Hydrophilic organic matter aerosol mixing ratio 2003-2019 80 km/0.75 CAMS Hydrophobic organic matter aerosol mixing ratio 2003-2019 80 km/0.75 CAMS Nitrogen monoxide 2003-2019 80 km/0.75 CAMS SO2 precursor mixing ratio 2003-2019 80 km/0.75 CAMS Sea salt aerosol (0.5 -5 m) mixing ratio 2003-2019 80 km/0.75 CAMS Sulphur dioxide 2003-2019 80 km/0.75 CAMS Formaldehyde 2003-2019 80 km/0.75 CAMS Hydrophilic black carbon aerosol mixing ratio 2003-2019 80 km/0.75 CAMS Hydrophobic black carbon aerosol mixing ratio 2003-2019 80 km/0.75 CAMS Hydroxyl radical 2003-2019 80 km/0.75 CAMS Methane (chemistry) 2003-2019 80 km/0.75 CAMS Nitrogen dioxide 2003-2019 80 km/0.75 CAMS Ozone/GEMS Ozone 2003-2019 80 km/0.75 CAMS Sea salt aerosol (0.03 -0.5 m) mixing ratio 2003-2019 80 km/0.75 CAMS Sea salt aerosol (5 -20 m) mixing ratio 2003-2019 80 km/0.75

  PublisherDOI

• Analysis of Hydrological Drought and Influencing Factors in Yunnan Province Using Integrated Gnss Observation Data and Xgboost Model

  SSRN Electronic Journal · 2025-01-01

  preprintOpen access
  PublisherDOI

• Assessment of regional and interannual variations in tropospheric ozone in chemical reanalyses

  Atmospheric chemistry and physics · 2025-01-14

  preprintOpen accessCorresponding

  Abstract. We evaluate regional and interannual variations in tropospheric ozone in five chemical reanalyses, consisting of the Copernicus Atmosphere Monitoring Service reanalysis (CAMSRA), the second-generation Tropospheric Chemistry Reanalysis (TCR-2), the GEOS-Chem reanalysis, the Community Multiscale Air Quality (CMAQ) regional analysis, and the Chinese air quality reanalysis (CAQRA). We find that there are large regional differences (about 10–15 nmol mol-1) in mean surface ozone between the reanalyses. GEOS-Chem has high ozone relative to the ensemble mean across most continental regions, whereas CAMSRA has low ozone. Comparison with surface ozone observations shows that the reanalyses are biased high relative to the observations, with surface ozone biases exceeding 10 nmol mol-1 in GEOS-Chem. We find that CAMSRA has the smallest bias with respect to the observations, with negative biases in Europe, and in the central and western US, and positive biases everywhere else. In the free troposphere the reanalyses are in good agreement, and the mean bias between the reanalyses and ozonesonde observations are small, less than 4 nmol mol-1 at 500 hPa. In addition, the correlations between the ozonesondes and the reanalyses are as high as 0.8 and 0.9 in the southern and northern midlatitudes respectively. The results suggest that chemical reanalyses should provide valuable information for quantifying variations in ozone in the free troposphere. However, to enhance the utility of the surface ozone analyses, improvements in the reanalyses are needed to better exploit assimilated observations to mitigate the impact of discrepancies in the model chemistry and ozone precursor emissions.

  PublisherDOI

• Intercomparison of global ground-level ozone datasets for health-relevant metrics

  Atmospheric chemistry and physics · 2025-11-18

  articleOpen accessCorresponding

  Abstract. Ground-level ozone is a significant air pollutant that detrimentally affects human health and agriculture. Global ground-level ozone concentrations have been estimated using chemical reanalyses, geostatistical methods, and machine learning, but these datasets have not been compared systematically. We compare six global ground-level ozone datasets (three chemical reanalyses, two machine learning, one geostatistics) relative to observations and against one another, for the ozone season daily maximum 8 h average mixing ratio, for 2006 to 2016. Comparing with global ground-level observations, most datasets overestimate ozone, particularly at lower observed concentrations. In 2016, across all stations, grid-to-grid R2 ranges from 0.50 to 0.75 and RMSE 4.25 to 12.22 ppb. Agreement with observed distributions is reduced at ozone concentrations above 50 ppb. Results show significant differences among datasets in global average ozone, as large as 5–10 ppb, multi-year trends, and regional distributions. For example, in Europe, the two chemical reanalyses show an increasing trend while other datasets show no increase. Among the six datasets, the share of population exposed to over 50 ppb varies from 61 % [28 %, 94 %] to 99 % [62 %, 100 %] in East Asia, 17 % [4 %, 72 %] to 88 % [53 %, 99 %] in North America, and 9 % [0 %, 58 %] to 76 % [22 %, 96 %] in Europe (2006–2016 average). Although sharing some of the same input data, we found important differences, likely from variations in approaches, resolution, and other input data, highlighting the importance of continued research on global ozone distributions. These discrepancies are large enough to impact assessments of health impacts and other applications.

  PublisherOA PDFDOI

• Supplementary material to "Assessment of regional and interannual variations in tropospheric ozone in chemical reanalyses"

  2025-01-14 · 3 citations

  preprint
  PublisherDOI

• <em>Weizmannia coagulans</em> BC99 Improve Cognitive Impairment Induced by Chronic Sleep Deprivation via Inhibiting the Brain and Intestine’s NLRP3 Inflammasome

  Preprints.org · 2025-02-24 · 1 citations

  preprintOpen access

  The consequences of chronic sleep deprivation include memory deficits and gastrointestinal dysfunction. Studies suggest that gut microbiota plays a causal role in cognitive impairment induced by chronic sleep deprivation, but the working mechanism of the microbiota-gut-brain axis remains unclear. In this study, a chronic sleep deprivation cognitive impairment model was established by sleep deprivation instrument, and Weizmannia coagulans BC99 was given by gavage for 4 weeks. BC99 improved cognitive abnormalities in novel object recognition tests induced by chronic sleep deprivation and showed behavior related to spatial memory in the Morris water maze test. W. coagulans BC99 reduced the heart mass index of sleep-deprived mice, increased the sleep-related neurotransmitters 5-HT and DA, decreased corticosterone and norepinephrine, and increased alpha diversity and community similarity. It reduced the abundance of harmful bacteria such as Olsenella, increased the abundance of beneficial bacteria such as Lactobacillus and Bifidobacterium, and promoted the production of short-chain fatty acids (SCFAs). W. coagulansBC99 also inhibits LPS translocation and the elevation of peripheral inflammatory factors by maintaining the integrity of the intestinal barrier and inhibiting the expression of the NLRP3 signaling pathway in the jejunum, thereby inhibiting NLRP3 inflammasome in the brain of mice and reducing inflammatory factors in the brain, providing a favorable environment for the recovery of cognitive function. The present study confirmed that W. coagulans BC99 ameliorated cognitive impairment in chronic sleep-deprived mice by improving gut microbiota, especially by promoting SCFAs production and inhibiting the NLRP3 signaling pathway in the jejunum and brain. These findings may help guide the treatment of insomnia or other sleep disorders through dietary strategies.

  PublisherOA PDFDOI

• Assessing the relative impacts of satellite ozone and its precursor observations to improve global tropospheric ozone analysis using multiple chemical reanalysis systems

  Atmospheric chemistry and physics · 2025-02-20 · 4 citations

  articleOpen access

  Abstract. Chemical reanalysis products have been produced by integrating various satellite observational data to provide comprehensive information on atmospheric composition. Five global chemical reanalysis datasets were used to evaluate the relative impacts of assimilating satellite ozone and its precursor measurements on surface and free-tropospheric ozone analyses for the year 2010. Observing system experiments (OSEs) were conducted with multiple reanalysis systems under similar settings to evaluate the impacts of reanalysis system selection on the quantification of observing system values. Without data assimilation, large discrepancies remained among the control runs owing to model biases. Data assimilation improved the consistency among the systems, reducing the standard deviation by 72 %–88 % in the lower troposphere through the lower stratosphere, while improving agreement with independent ozonesonde observations. The OSEs suggested the importance of precursor measurements, especially from tropospheric NO2 columns, for improving ozone analysis in the lower troposphere, with varying influences among the systems (increases in global lower-tropospheric ozone by 0.1 % in GEOS-Chem and 7 % in Tropospheric Chemistry Reanalysis version 2 (TCR-2), with only NO2 assimilation). Adjustments made by direct ozone assimilation showed similar vertical patterns between the TCR-2 and IASI-r systems, with increases of 6 %–22 % and decreases of 2 %–21 % in the middle and upper troposphere, respectively, reflecting the biases of the forecast models. These results suggest the importance of considering the effects of the forecast model performance and data assimilation configurations when assessing the observing system impacts to provide unbiased evaluations of satellite systems and to guide the design of future observing systems.

  PublisherDOI

• What can we learn about tropospheric OH from satellite observations of methane?

  Atmospheric chemistry and physics · 2025-03-11 · 4 citations

  articleOpen access

  Abstract. The hydroxyl radical (OH) is the main oxidant in the troposphere and controls the lifetime of many atmospheric pollutants, including methane. Global annual-mean tropospheric OH concentrations ([OH‾]) have been inferred since the late 1970s using the methyl chloroform (MCF) proxy. However, concentrations of MCF are now approaching the detection limit, and a replacement proxy is urgently needed. Previous inversions of GOSAT (Greenhouse Gases Observing Satellite) satellite measurements of methane in the shortwave infrared (SWIR) have shown success in quantifying [OH‾] independently of methane emissions, and observing system simulations have suggested that satellite measurements in the thermal infrared (TIR) may provide additional constraints on OH. Here we combine SWIR and TIR satellite observations from the GOSAT and AIRS instruments, respectively, in a 3-year (2013–2015) analytical Bayesian inversion optimizing both methane emissions and OH concentrations. We examine how much information can be obtained about the interannual, seasonal, and latitudinal features of the OH distribution. We use information from MCF data and the ACCMIP ensemble of global atmospheric chemistry models to construct a full prior error covariance matrix for OH concentrations for use in the inversion. This is essential to avoid an overfitting of the observations. Our results show that GOSAT alone is sufficient to quantify [OH‾] and its interannual variability independently of methane emissions and that AIRS adds little information. The ability to constrain the latitudinal variability of OH is limited by strong error correlations. There is no information on OH at midlatitudes, but there is some information on the NH/SH interhemispheric ratio, showing this ratio to be lower than currently simulated in models. There is also some information on the seasonal variation in OH concentrations, although it mainly confirms the variation simulated by the models.

  PublisherOA PDFDOI

Frequent coauthors

• Daven K. Henze

  University of Colorado Boulder

  109 shared

• Nicolas Theys

  50 shared

• Daniel J. Jacob

  Harvard University

  49 shared

• Xiao Lu

  Peking University

  48 shared

• John R. Worden

  43 shared

• Melissa P. Sulprizio

  Harvard University

  43 shared

• Yuzhong Zhang

  43 shared

• Robert J. Parker

  National Centre for Earth Observation

  40 shared

Labs

• Qu GroupPI

  Atmospheric Chemistry Modeling at NC State