When a Generalized Linear Model Meets Bayesian Maximum Entropy: A Novel Spatiotemporal Ground-Level Ozone Concentration Retrieval Method

In China, ground-level ozone has shown an increasing trend and has become a serious ambient pollutant. An accurate spatiotemporal distribution of ground-level ozone concentrations (GOCs) is urgently needed. Generalized linear models (GLMs) and Bayesian maximum entropy (BME) models are practical for predicting GOCs. However, GLMs have limited capacity to capture temporal variations and can miss some short-term and regional patterns, while the performance of BME models may degrade in cases of sparse or imperfect monitoring networks. Thus, to predict nationwide 1 km monthly average GOCs for China, we designed a novel hybrid model containing three modules. (1) A GLM was established to accurately describe the variability in GOCs in the space domain. (2) A BME model incorporating GLM residuals was employed to capture the temporal variability of GOCs in detail. (3) A combination of GLM and BME models was developed based on the specific broad range of each submodel. According to the cross-validation results, the hybrid model exhibited superior performance, with coefficient of determination (R2) values of 0.67. The predictive performance of the large-scale and high-resolution hybrid model is superior to that in previous studies. The nationwide spatiotemporal variability of the GOCs derived from the hybrid model shows that they are valuable indicators for ground-level ozone pollution control and prevention in China.

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Current and future occurrences of health-relevant, compound heat and ozone pollution events in six European ozone-temperature regions

10.5194/ems2021-61 ◽

2021 ◽

Author(s):

Sally Jahn ◽

Elke Hertig

Keyword(s):

Climate Change ◽

Air Temperature ◽

Large Scale ◽

Coupled Model ◽

Ground Level ◽

Future Climate Change ◽

Ozone Pollution ◽

Ground Level Ozone ◽

Ozone Concentrations ◽

Elevated Ozone

Air pollution and heat events present two major health risks, both already independently posing a significant threat to human health and life. High levels of ground-level ozone (O3) and air temperature often coincide due to the underlying physical relationships between both variables. The most severe health outcome is in general associated with the co-occurrence of both hazards (e.g. Hertig et al. 2020), since concurrent elevated levels of temperature and ozone concentrations represent a twofold exposure and can lead to a risk beyond the sum of the individual effects. Consequently, in the current contribution, a compound approach considering both hazards simultaneously as so-called ozone-temperature (o-t-)events is chosen by jointly analyzing elevated ground-level ozone concentrations and air temperature levels in Europe.Previous studies already point to the fact that the relationship of underlying synoptic and meteorological drivers with one or both of these health stressors as well as the correlation between both variables vary with the location of sites and seasons (e.g. Otero et al. 2016; Jahn, Hertig 2020). Therefore, a hierarchical clustering analysis is applied to objectively divide the study domain in regions of homogeneous, similar ground-level ozone and temperature characteristics (o-t-regions). Statistical models to assess the synoptic and large-scale meteorological mechanisms which represent main drivers of concurrent o-t-events are developed for each identified o-t-region.Compound elevated ozone concentration and air temperature events are expected to become more frequent due to climate change in many parts of Europe (e.g. Jahn, Hertig 2020; Hertig 2020). Statistical projections of potential frequency shifts of compound o-t-events until the end of the twenty-first century are assessed using the output of Earth System Models (ESMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6).Hertig, E. (2020) Health-relevant ground-level ozone and temperature events under future climate change using the example of Bavaria, Southern Germany. Air Qual. Atmos. Health. doi: 10.1007/s11869-020-00811-zHertig, E., Russo, A., Trigo, R. (2020) Heat and ozone pollution waves in Central and South Europe- characteristics, weather types, and association with mortality. Atmosphere. doi: 10.3390/atmos11121271Jahn, S., Hertig, E. (2020) Modeling and projecting health&#8208;relevant combined ozone and temperature events in present and future Central European climate. Air Qual. Atmos. Health. doi: 10.1007/s11869&#8208;020&#8208;009610Otero N., Sillmann J., Schnell J.L., Rust H.W., Butler T. (2016) Synoptic and meteorological drivers of extreme ozone concentrations over Europe. Environ Res Lett. doi: 10.1088/ 1748-9326/11/2/024005

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Can Elderly Lungs Cope with Urban Concentrations of Ground-Level Ozone? Experiences from a Large-Scale Multicenter Exposure Chamber Study

American Journal of Respiratory and Critical Care Medicine ◽

10.1164/rccm.201801-0108ed ◽

2018 ◽

Vol 197 (10) ◽

pp. 1245-1246

Author(s):

Thomas Sandström ◽

Anders Blomberg ◽

Jenny A. Bosson

Keyword(s):

Large Scale ◽

Ground Level ◽

Exposure Chamber ◽

Ground Level Ozone ◽

Chamber Study

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Spatiotemporal variability of ground-level ozone and influence of smoke in Treasure Valley, Idaho

Atmospheric Research ◽

10.1016/j.atmosres.2012.12.005 ◽

2013 ◽

Vol 124 ◽

pp. 44-52 ◽

Cited By ~ 13

Author(s):

I.G. Kavouras ◽

D.W. DuBois ◽

V. Etyemezian ◽

G. Nikolich

Keyword(s):

Ground Level ◽

Spatiotemporal Variability ◽

Ground Level Ozone

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Space-time mapping of ground-level PM2.5 and NO2 concentrations in heavily polluted northern China during winter using the Bayesian maximum entropy technique with satellite data

Air Quality Atmosphere & Health ◽

10.1007/s11869-017-0514-8 ◽

2017 ◽

Vol 11 (1) ◽

pp. 23-33 ◽

Cited By ~ 12

Author(s):

Qutu Jiang ◽

George Christakos

Keyword(s):

Maximum Entropy ◽

Satellite Data ◽

Ground Level ◽

Northern China ◽

Space Time ◽

Bayesian Maximum Entropy ◽

Entropy Technique

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Relationship Between Large-Scale Atmospheric States, Subsidence, Static Stability and Ground-Level Ozone in Illinois, USA

Water Air & Soil Pollution ◽

10.1007/s11270-005-9021-x ◽

2006 ◽

Vol 171 (1-4) ◽

pp. 111-133 ◽

Cited By ~ 6

Author(s):

Gidon Eshel ◽

Joseph J. Bernstein

Keyword(s):

Large Scale ◽

Ground Level ◽

Static Stability ◽

Ground Level Ozone

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Health-relevant, concurrent ground-level ozone and temperature events in recent and future European climate

10.5194/egusphere-egu21-451 ◽

2021 ◽

Author(s):

Sally Jahn ◽

Elke Hertig

Keyword(s):

Machine Learning ◽

Human Health ◽

Health Effects ◽

Central Europe ◽

Air Temperature ◽

Large Scale ◽

Ground Level ◽

Ground Level Ozone ◽

Ozone Concentrations ◽

European Climate

Temperature extremes like hot days or prolonged episodes of high air temperature like heat waves can cause adverse human health effects. Heat-related mortality only represents the extreme end of a variety of possible health outcomes like heat exhaustion or heat stroke.Exposure to ground-level ozone provokes negative impacts on human health primarily affecting the cardio-pulmonary system causing respiratory or cardiovascular diseases. These diseases include, but are not limited to, lung inflammation and tissue damage, asthma, heart attacks or heart failure.High levels of ozone and temperature often coincide due to the underlying ozone formation characteristics. As synergistic effects lead to a risk beyond the sum of their individual effects, the co-occurrence of elevated levels of air temperature and ground-level ozone concentrations represents an even intensified human health risk.The current contribution deals with statistical models and analysis of the interplay between large-scale meteorological and synoptic conditions, prevailing air pollution levels and combined ozone and temperature events under present and future climatic conditions. In this context, meteorological mechanisms representing main drivers of these concurrent ozone and temperature events were identified. Large-scale atmospheric circulation dynamics and their relationships with ground-level ozone and temperature conditions were evaluated.The methodological focus was primary on statistical modeling approaches and different machine learning methods. Self-Organizing Maps, an artificial neural network algorithm based on unsupervised machine learning, were used to classify synoptic types based on daily mean sea level pressure reanalysis data. The resulting synoptic types were evaluated with regard to the European ozone and temperature characteristics in order to identify types associated with high ozone and temperature. Regression analyses with e.g. shrinking methods were used to identify main predictors for concurrent ozone and temperature events. Due to data availability and research foci, two varying time windows from 1993 to 2012 as well as from 2004 to 2018 were used within the study. The European area built the regional focus.Anthropogenic-induced global climate change affects not only mean but also extreme temperatures as well as associated ground-level ozone concentrations due to changing synoptic circulation and chemical environment conditions. Future frequency changes of concurrent ozone and temperature events were evaluated exemplarily for Central Europe. Statistical downscaling projections until the end of the twenty-first century were assessed by using the output of seven models of the Coupled Model Intercomparison Project Phase 5 (CMIP5). A sharp increase was projected under RCP4.5 and RCP8.5 scenario assumptions. Respective multi-model mean changes amounted to 8.94% and 16.84% as well as 13.33% and 37.52% for mid- (2031&#8211;2050) and late-century (2081&#8211;2100) European climate, respectively (Jahn and Hertig 2020). Hotspot regions with more frequent occurrences of these combined events in Central Europe were identified for which, due to their associated individual and combined health effects, a higher future vulnerability can be expected.

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