4-9 September 2022, Bonn, Germany
Enter gather.town

UP2.2

Analysis, monitoring and prediction of chemical weather, air pollution, and the effects of COVID-19

This session welcomes presentations discussing issues related to analysis, monitoring and prediction of topics related to chemical weather including air pollution. We are welcoming abstracts on the implementation and application of air quality forecast and attribution models around the world, the development and evaluation of air quality models, the downscaling techniques particularly focusing on regions with severe air pollution problems, the co-design and co-development of air quality products and services, and the knowledge transfer and capacity building activities of air quality related information.
A special aspect of the session will be the effects of the still ongoing COVID-19 pandemic that strongly affects the society-environment interactions. Restrictions associated with the pandemic have led to significant changes of the pollution and exposure levels across the world. This includes, i.a., emission and pollution changes associated to the countries and regions lockdowns stringency, and the influence of different atmospheric conditions (e.g. air quality, solar radiation, atmospheric dynamics, relative humidity and temperature) in the spread of COVID-19.

Co-organized by ES1
Conveners: Francesca Costabile, Cathy Wing Yi Li, Guy Brasseur | Co-conveners: Leena Järvi, Jan Semenza, Rajesh Kumar
Orals
| Mon, 05 Sep, 14:00–15:30 (CEST)|Room HS 1
Posters
| Attendance Mon, 05 Sep, 16:00–17:30 (CEST) | Display Mon, 05 Sep, 08:00–18:00|b-IT poster area

Mon, 5 Sep, 16:00–17:30

Chairpersons: Cathy Wing Yi Li, Francesca Costabile

EMS2022-342
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Onsite presentation
Ji Won Yoon et al.

  The environmental problems related to air pollution have been increasing, especially in East Asia, due to human activities, including high energy consumption and rapid economic growth. In order to address the air pollution problems, it is essential not only to improve the national and local air pollution control measures but also to enhance the air quality forecast skill through a numerical prediction system. The performance of numerical air quality prediction is significantly dependent on the land surface and the PBL parameterization schemes in a coupled atmosphere-chemistry prediction system, such as the Weather Research and Forecasting (WRF) model coupled with Chemistry (WRF-Chem).

  In this study, to improve the air quality prediction performance in East Asia, we built an intelligent optimization system by coupling the micro-genetic algorithm (μGA) and the WRF-Chem model --- the WRF-Chem-μGA system. This system can find an optimal set of physical parameterization schemes in WRF-Chem to improve the air quality forecasting.

 Before optimization, we selected several cases by considering the synoptic weather patterns according to the sources and the transport routes of the sand dust storms that affected Korea. As a preliminary study, we aim to obtain the optimal set of the land surface and PBL schemes via the intelligent optimization system for each case, which is the most suitable for predicting some Asian sand dust storm (SDS) events over Korea. Overall, our preliminary results show that the WRF-Chem with the optimized set of parameterization schemes produces better results than that with non-optimized scheme sets in forecasting the selected SDS events in East Asia.

How to cite: Yoon, J. W., Lee, E., Lim, S., Lee, S., and Park, S. K.: Combinational Optimization of Physical Parameterization Schemes to Improve Air Quality Prediction Using Intelligent Optimization System, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-342, https://doi.org/10.5194/ems2022-342, 2022.

EMS2022-346
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Onsite presentation
Seungyeon Lee et al.

In air quality prediction, initial conditions, based on both atmospheric and aerosol/chemistry observations, are essentially required for a coupled atmosphere-chemistry model. In general, we have more accurate model results with a higher amount and quality of observations; however, forecast errors in a region of interest may grow from an initial error in a specific upstream region, primarily due to the lack of observations therein. Therefore, finding the upstream areas from which a small initial error can grow into significant forecast errors in the region of interest is essential for the strategic enhancement of observations to improve numerical air quality prediction. The conditional nonlinear optimal perturbation for initial conditions (CNOP-I) represents the initial error that can lead to the most significant error at the forecast time, which enables us to identify the sensitive regions as a considerable initial error value. Thus, CNOP-I is a suitable tool for targeted (adaptive) observations. The goal of this study is twofold: 1) to classify the synoptic patterns for sand dust storm cases by using the principal component analysis (PCA), and 2) to identify the sensitive areas in targeted observations for air quality prediction via CNOP-I. We focus on the Asian dust storm events that occurred over South Korea during the last 32 years (1990—2021). We expect to improve air quality forecasts by classifying the synoptic situations that bring about severe dust storm outbreaks in South Korea and by identifying the upstream areas for targeted observations to which we can potentially enhance observations through international collaborations.

How to cite: Lee, S., Qin, X., Yoon, J. W., Lee, E., Lim, S., and Park, S. K.: Classifying Synoptic Patterns and Identifying Sensitive Areas for Targeted Observations to Improve Air Quality Prediction over South Korea, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-346, https://doi.org/10.5194/ems2022-346, 2022.

Orals

14:00–14:15
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EMS2022-597
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Presentation form not yet defined
Guy Brasseur et al.

We use the global Community Earth System Model to investigate the response of secondary pollutants (ozone O3, secondary organic aerosols SOA) in response to modified emissions of primary pollutants during the COVID-19 pandemic. We use an estimate of the reduction in surface and aircraft emissions to derive the changes in the chemical composition of the atmosphere. We quantify the respective effects of the reductions in NOx and in VOC emissions, which, in most cases, affect oxidants in opposite ways. Using model simulations, we show that, relative to a situation in which the emission reductions are ignored, the ozone concentration increased only in a few NOx-saturated regions during the winter months of the pandemic when the titration of this molecule by NOx was reduced. In other regions, where ozone is NOx-controlled, the concentration of ozone decreased.  SOA concentrations decrease in response to the concurrent reduction in the NOx and VOC emissions. Zonally averaged ozone concentrations in the free troposphere during Northern Hemisphere spring and summer were 5 to 15% lower than 19-year climatological values, in good quantitative agreement with ozone observations. We examine the response in free tropospheric ozone at different latitudes and specifically in the southern hemisphere, the tropics, the northern hemisphere mid-latitudes and polar region. About one third of this anomaly is attributed to the drastic reduction in air traffic during the pandemic, another third to reductions in surface emissions, the remainder to 2020 meteorological conditions, including the exceptional springtime Arctic stratospheric ozone depletion. We compare calculated changes in the vertical ozone profiles with values derived from ozone sonde observations.

How to cite: Brasseur, G., Gaubert, B., Bouarar, I., Steinbrecht, W., Granier, C., and Doumbia, T.: The response of secondary chemical species to COVID-19 related emission distrubances, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-597, https://doi.org/10.5194/ems2022-597, 2022.

14:15–14:30
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EMS2022-699
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CC
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Onsite presentation
Tomas Halenka and Ranjeet Sokhi

While overall the global warming with the causes and global processes connected to well-mixed CO2, and its impacts on global to continental scales are well understood with a high level of confidence, there are knowledge gaps concerning the impact of many other non-CO2 radiative forcers leading to low confidence in the conclusions. This relates mainly to specific anthropogenic and natural precursor emissions of short-lived GHGs and aerosols and their precursors. These gaps and uncertainties also exist in their subsequent effects on atmospheric chemistry and climate, through direct emissions dependent on changes in e.g., agriculture production and technologies based on scenarios for future development as well as feedbacks of global warming on emissions, e.g., permafrost thaw. In addition to the atmospheric radiative forcing (gaseous or aerosols), albedo changes connected to land use and land cover can play a role, depending on the adaptation or mitigation measures included in different scenarios.

Thus, the main goal of the new EC Horizon Europe project FOCI (accepted within the call HORIZON-CL5-2021-D1-01-0 Improved understanding of greenhouse gas fluxes and radiative forcers, including carbon dioxide removal technologies), which will be presented, is to assess the impact of key radiative forcers, where and how they arise, the processes of their impact on the climate system, to find and test an efficient implementation of these processes into global Earth System Models and into Regional Climate Models, eventually coupled with CTMs, and finally to use the tools developed to investigate mitigation and/or adaptation policies incorporated in selected scenarios of future development targeted at Europe and other regions of the world. We will develop new regionally tuned scenarios based on improved emissions to assess the effects of non-CO2 forcers. Mutual interactions of the results and climate services producers and other end-users will provide feedbacks for the specific scenarios preparation and potential application to support the decision making, including climate policy.

How to cite: Halenka, T. and Sokhi, R.: Non-CO2 Forcers and Their Climate, Weather, Air Quality and Health Impacts – New Project FOCI, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-699, https://doi.org/10.5194/ems2022-699, 2022.

14:30–14:45
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EMS2022-164
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Onsite presentation
Cathy Wing Yi Li et al.

AQ-WATCH (Air Quality: Worldwide Analysis and Forecasting of Atmospheric Composition for Health) is an international consortium, which co-develops and co-produces tailored products and services derived from space and in situ observational data for improving air quality forecasts and attribution. For this purpose, AQ-WATCH develops a supply chain leading to innovative downstream products and services for providing air quality information. These prototypes are based on existing space and in situ observations of air quality and tailored to the identified needs of international users. The project allows small and medium enterprises to integrate datasets associated with earth’s observations with advanced predictive models. These innovative products and services are aimed at improving public health and optimizing service provided by the energy sector in different regions of the world. The project also creates opportunities for new environmental technologies and enhances the interaction between research institutions, stakeholders and enterprises in support of the world-wide effort to improve air quality.

The AQ-WATCH products and services are organized into 5 modules allowing users to access historical and air pollution data as well as air quality forecasts at a global and regional scale, to compare different air pollution reduction scenarios, to assess the effect of wildfires or fracking activities on air quality, and to derive the changes in solar irradiance due to the presence of dust in the atmosphere. These 5 modules include: (1) Global and regional air quality atlas, (2) Air quality attribution & mitigation, (3) Dust and fire forecasting system, (4) Fracking analysis, and (5) Air quality forecast. In this presentation, apart from an overview of the AQ-WATCH project and its products, extra focus will be put on the last product module, Air quality forecast, in particular on the effort of the consortium to provide air quality forecasts from their hosting regional models and the execution of the AQ-WATCH multi-model air quality forecast system.

How to cite: Li, C. W. Y., Brasseur, G., Sofiev, M., Timmermans, R., Kumar, R., Pfister, G., Mo, D., Granier, C., Doumbia, T., Basart, S., Salvi, O., Caillard, B., and Boose, Y.: Introduction to the AQ-WATCH project and its multi-model air quality forecast system , EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-164, https://doi.org/10.5194/ems2022-164, 2022.

14:45–15:00
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EMS2022-289
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Onsite presentation
Evan Couzo and Christopher Godfrey

Routine ambient air quality monitoring is mandated by U.S. federal law, but many regions of the U.S. are sparsely monitored due in part to the high cost of regulatory monitors.  Low-cost sensors can fill data gaps when it is neither feasible nor cost-effective to expand regulatory monitoring networks.  Initial comparisons of ambient measurements from the PurpleAir PA-II sensor – a low-cost (<$300 USD) and commercially available sensor that measures fine particulate matter (PM2.5) using a laser particle counter – with nearby (~2 km) regulatory PM2.5 monitors maintained by the Asheville Buncombe Air Quality Agency in Asheville, North Carolina, indicate general agreement.  Continuous data collection began in January 2022, thus capturing a range of atmospheric conditions and seasonal changes in PM2.5 concentrations.  High water vapor content is known to affect the performance of optical particle counters such as the PA-II, so additional summertime measurements may reveal persistent biases in air quality measurements in these conditions.

The PA-II sensor also measures temperature, relative humidity, and barometric pressure.  Preliminary analyses compare the sensor’s measurements of these environmental variables to co-located research-grade meteorological instrumentation, including Campbell Scientific, Inc. 107 and 109 temperature probes, a Vaisala HMP45C temperature and relative humidity probe, and a Vaisala PTB101B barometer. Results reveal an expected high temperature bias of several degrees Fahrenheit caused by the small amount of heat generated by the electronics contained in the PA-II’s housing. A low relative humidity bias is a consequence of the temperature performance. Barometric pressure measurements from the PA-II sensor are consistently high by about 2-3 hPa.

How to cite: Couzo, E. and Godfrey, C.: A comparison of the PurpleAir PA-II sensor to both regulatory particulate matter monitors and meteorological instrumentation in Asheville, North Carolina, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-289, https://doi.org/10.5194/ems2022-289, 2022.

15:00–15:15
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EMS2022-322
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Onsite presentation
The Korean Integrated Model (KIM) coupled with atmospheric chemistry model: plans and preliminary results
(withdrawn)
Shin-Young Park and Soo Ya Bae
15:15–15:30
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EMS2022-604
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Onsite presentation
Lisa Schielicke et al.

The Covid-Pandemic determines social life since the beginning of 2020. Several lockdowns forced people to reduce their social contacts, especially private contacts during their spare time. For fear of infection, some people even reduced their contacts over the whole time of the pandemic. The global virus pandemic is characterised by seasonal waves with periodically increasing and decreasing incidence. This periodic variability depends on many factors. Of central relevance are all kinds of social meetings and gatherings.  Covid-19 is an airborne disease and, hence, indoor meetings and activities without masks pose a significant higher risk of infection than outdoor meetings. The decision to meet indoors or outdoors depends largely on the waether -- amongst other parameters such as the kind of activity (sport, picnic, cooking, gaming, singing) or the composition and background of the  group (band, sports group, study group, students, parents with kids). The type of outdoor activity is strongly dependent on the weather.

In a two-way approach of surveys and data analysis, we want to find out how weather parameters such as temperature, wind, precipitation, cloudiness, etc. influence the decision to meet indoors or outdoors. On the one hand, surveys and interviews of students give insight into the special situation oft he social life of students during the pandemic. On the other hand, DWD weather observations of several German cities, contact data and infections numbers are statistically analysed in order to detect possible relationships between the parameters. In this work, we will present first results of this recently started project.

How to cite: Schielicke, L., Bohmann, M., and Ertz, P.: Changes of social contacts due to the Covid-19 pandemic and the dependence on weather parameters, EMS Annual Meeting 2022, Bonn, Germany, 5–9 Sep 2022, EMS2022-604, https://doi.org/10.5194/ems2022-604, 2022.

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