8th IWAQFR, Toronto, Ontario. January 10–12, 2017

Slides:



Advertisements
Similar presentations
The Regional Chemical Transport Model over Northeast Asia Area Operated by Japan Meteorological Agency A. Kamada*, M. Ikegami, H. Naoe, M. Kajino, M. Deushi,
Advertisements

David J. Sailor1 and Hongli Fan2 1. Portland State University
A numerical simulation of urban and regional meteorology and assessment of its impact on pollution transport A. Starchenko Tomsk State University.
CO budget and variability over the U.S. using the WRF-Chem regional model Anne Boynard, Gabriele Pfister, David Edwards National Center for Atmospheric.
The Use of High Resolution Mesoscale Model Fields with the CALPUFF Dispersion Modelling System in Prince George BC Bryan McEwen Master’s project
Atmospheric conditions associated with high and low summer ozone levels in the lower troposphere over the eastern Mediterranean and ship borne observations.
Assimilation of TES O 3 data in GEOS-Chem Mark Parrington, Dylan Jones, Dave MacKenzie University of Toronto Kevin Bowman Jet Propulsion Laboratory California.
Integrating satellite observations for assessing air quality over North America with GEOS-Chem Mark Parrington, Dylan Jones University of Toronto
REFERENCES Maria Val Martin 1 C. L. Heald 1, J.-F. Lamarque 2, S. Tilmes 2 and L. Emmons 2 1 Colorado State University 2 NCAR.
October 17, CMAS 2006 conference
Future prediction of tropospheric ozone over south and east Asia in 2030 Satoru Chatani* Toyota Central R&D Labs., Inc. Markus Amann and Zbigniew Klimont.
Examination of the impact of recent laboratory evidence of photoexcited NO 2 chemistry on simulated summer-time regional air quality Golam Sarwar, Robert.
Modeling Studies of Air Quality in the Four Corners Region National Park Service U.S. Department of the Interior Cooperative Institute for Research in.
Impact of Emissions on Intercontinental Long-Range Transport Joshua Fu, Yun-Fat Lam and Yang Gao, University of Tennessee, USA Rokjin Park, Seoul National.
Earth&Atmospheric Sciences, Georgia Tech Modeling the impacts of convective transport and lightning NOx production over North America: Dependence on cumulus.
1 Using Hemispheric-CMAQ to Provide Initial and Boundary Conditions for Regional Modeling Joshua S. Fu 1, Xinyi Dong 1, Kan Huang 1, and Carey Jang 2 1.
Diurnal Variations of Tropical Convection Ohsawa, T., H. Ueda, T. Hayashi, A. Watanabe, and J. Matsumoto, 2001 : Diurnal Variations of Convective Activity.
The effect of pyro-convective fires on the global troposphere: comparison of TOMCAT modelled fields with observations from ICARTT Sarah Monks Outline:
Wildland Fire Impacts on Surface Ozone Concentrations Literature Review of the Science State-of-Art Ned Nikolov, Ph.D. Rocky Mountain Center USDA FS Rocky.
Application of Models-3/CMAQ to Phoenix Airshed Sang-Mi Lee and Harindra J. S. Fernando Environmental Fluid Dynamics Program Arizona State University.
CITES 2005, Novosibirsk Modeling and Simulation of Global Structure of Urban Boundary Layer Kurbatskiy A. F. Institute of Theoretical and Applied Mechanics.
On the interplay between upper and ground levels dynamics and chemistry in determining the surface aerosol budget Gabriele Curci 1, L. Ferrero 2, P. Tuccella.
Impact of high resolution modeling on ozone predictions in the Cascadia region Ying Xie and Brian Lamb Laboratory for Atmospheric Research Department of.
Itsushi UNO*, Youjiang HE, Research Institute for Applied Mechanics, Kyushu University, Kasuga, Fukuoka, JAPAN Toshimasa OHARA, Jun-ichi KUROKAWA, Hiroshi.
Applications of Models-3 in Coastal Areas of Canada M. Lepage, J.W. Boulton, X. Qiu and M. Gauthier RWDI AIR Inc. C. di Cenzo Environment Canada, P&YR.
1 Impact on Ozone Prediction at a Fine Grid Resolution: An Examination of Nudging Analysis and PBL Schemes in Meteorological Model Yunhee Kim, Joshua S.
C. Hogrefe 1,2, W. Hao 2, E.E. Zalewsky 2, J.-Y. Ku 2, B. Lynn 3, C. Rosenzweig 4, M. Schultz 5, S. Rast 6, M. Newchurch 7, L. Wang 7, P.L. Kinney 8, and.
Office of Research and Development Atmospheric Modeling and Analysis Division, National Exposure Research Laboratory Simple urban parameterization for.
Seasonal Modeling of the Export of Pollutants from North America using the Multiscale Air Quality Simulation Platform (MAQSIP) Adel Hanna, 1 Rohit Mathur,
Impact of the changes of prescribed fire emissions on regional air quality from 2002 to 2050 in the southeastern United States Tao Zeng 1,3, Yuhang Wang.
AMMA WP4.1.3 meeting at Service d'Aéronomie, Jussieu, Paris 2-3 July D modeling with Méso-NH over West Africa Marielle SAUNOIS, Céline MARI, Valérie.
August 1999PM Data Analysis Workbook: Characterizing PM1 Characterizing Ambient PM Concentrations and Processes What are the temporal, spatial, chemical,
Introduction North China, or Huabei region, located between 32°- 42°N latitude in eastern China, is one of the most severely polluted regions in China.
Impacts of Meteorological Conditions Modified by Urban Expansion on Surface Ozone over Yangtz River Delta and Pearl River Delta region, China Xuemei Wang,
Eastern US Transport Climatology During Average, High and Low Ozone Days Bret A. Schichtel and Rudolf B. Husar Center for Air Pollution Impact and Trend.
Long-term measurements of surface ozone at remote and rural sites in China Xiaobin Xu 1, Weili Lin 1,2 1 Chinese Academy of Meteorological Sciences Key.
Forecasting Air quality in China Using CAMS Boundary Conditions: the PANDA Project Guy P. Brasseur and Idir Bouarar June 206.
Ship emission effect on Houston Ship Channel CH2O concentration ——study with high resolution model Ye Cheng.
Yuqiang Zhang1, Owen R, Cooper2,3, J. Jason West1
TOPOGRAPHICALLY INDUCED CONVECTIVE CLOUD PATTERNS
Jan Geletič, Petr Dobrovolný
Meso-scale Model's Results
The impacts of dynamics and biomass burning on tropical tropospheric Ozone inferred from TES and GEOS-Chem model Junhua Liu
Analysis of tropospheric ozone long-term lidar and surface measurements at the JPL-Table Mountain Facility site, California Maria J. Granados-Muñoz and.
Urban Heat Island (UHI)
Urban Heat Island (UHI)
The ability for the ocean to absorb and store energy from the sun is due to… The transparency of the water that allows the sun’s ray to penetrate deep.
16th Annual CMAS Conference
Charles University in Prague
C. Nolte, T. Spero, P. Dolwick, B. Henderson, R. Pinder
Impact of the vertical resolution on Climate Simulation using CESM
AIR POLLUTION Page: 540 Figure 19.4 Haze, smoke (particulate matter), and other pollutants cover the Los Angeles skyline during the summer of 2009.
Fig. 2 shows the relationship between air temperature and relative humidity. 2 (a) (i) Describe the relationship shown in Fig. 2. [3] (ii) State.
Quantification of Lightning NOX and its Impact on Air Quality over the Contiguous United States Daiwen Kang, Rohit Mathur, Limei Ran, Gorge Pouliot, David.
The course finished last lesson but what are you missing?
Modeling the impacts of green infrastructure land use changes on air quality and meteorology—case study and sensitivity analysis in Kansas City Yuqiang.
Aura Science Team meeting
Fig. 2 shows the relationship between air temperature and relative humidity. (a) (i) Describe the relationship shown in Fig. 2. [3] (ii) State.
The Double Dividend of Methane Control
Analysis of CO in the tropical troposphere using Aura satellite data and the GEOS-Chem model: insights into transport characteristics of the GEOS meteorological.
Satellite Remote Sensing of Ozone-NOx-VOC Sensitivity
Space-based Diagnosis of Surface Ozone Sensitivity to Anthropogenic Emissions Randall Martin Aaron Van Donkelaar Arlene Fiore.
By Dr. Robert M MacKay Clark College Physics & Meteorology
Deborah Luecken and Golam Sarwar U.S. EPA, ORD/NERL
Global atmospheric changes and future impacts on regional air quality
Intercontinental Transport, Hemispheric Pollution,
Linking Ozone Pollution and Climate Change:
1 GFDL-NOAA, 2 Princeton University, 3 BSC, 4 Cerfacs, 5 UCAR
Effects of global change on U.S. ozone air quality
Ming-Dah Chou Department of Atmospheric Sciences
Presentation transcript:

8th IWAQFR, Toronto, Ontario. January 10–12, 2017 Session 4: Urban and High-Resolution Air Quality Modelling Impacts of urban heat-island circulation on distributions of air pollutants over Tokyo *Makoto Deushi1,2, Mizuo Kajino1, Toshinori Aoyagi1,2 1 Meteorological Research Institute, 2 Japan Meteorological Agency Thank you for introducing me. I am Makoto Deushi of Meteorological Research Institute, Japan. Today, I’d like to talk to you about “Impacts of urban heat-island circulation on distributions of air pollutants over Tokyo”.

Outline The effect of urbanization on local meteorological phenomena has been the subject of much research in Japan (e.g. Fujibe 2009). However, the impacts of urban heat-island (UHI) on distributions of air pollutants over the Tokyo area, Japan, have received less attention so far. We investigate the impacts of UHI circulation by using an online regional chemical transport model coupled to an urban canopy model. Here is an outline of this talk. The effect of urbanization on local meteorological phenomena has been the subject of much research in Japan. However, the impacts of urban heat-island on distributions of air pollutants such as ozone and NOx over the Tokyo region have received less attention so far. In this study, we investigate the impacts of urban heat island circulation by using an online regional chemical transport model with an urban canopy model.

Urban heat-island in summer over Tokyo JMA NHM simulation Sea-breeze This slide shows simulated surface temperature in a typical summer sunny day over the Tokyo and the suburban areas. We used the meso-scale non-hydrostatic model coupled to the urban canopy model for this simulation. This model has been developed at Japan Meteorological Agency, called JMA NHM. As shown in the left figure, the high temperature area over 35 degrees is simulated at 15 local time. The intrusion of sea breeze is deep over the land. The estimated urbanization effect of the Tokyo area on the temperature at this time are shown in the right figure. As you can see, the temperature increase due to the urbanization is over 3 degrees at maximum in this simulation. The see-breeze weakens due to the urban-induced local circulation anomaly. Temperature at 15 JST Diff. in Temperature between URBAN run and NO_URB run

Relation between see-breeze and ozone in summer 14 JST 18 JST This slide shows composite patterns of Ox concentration and surface wind vectors at 14 and 18 local times in Tokyo and the suburban areas. Colored circles indicate the composite of observed Ox concentration under fair weather conditions in summer. The maximum Ox concentration at 14 local time are observed near the Tokyo bay, where is both the most populous and largest industrialized area in Japan. On the other hand, at 18 local time, the maximum Ox concentrations are found in a suburban area to the north-west of Tokyo. The daytime sea-breeze from the south-east mainly transports pollutants such as ozone and NOx to the northwest inland area. One of the questions is how the urban heat island circulation influence on this relation between see-breeze and distributions of the pollutants in summer. . Endo et al. (2013)

Model domain with terrain height Air quality model Model name: NHM-Chem (Kajino et al., 2012) Meso-scale atmospheric model: JMA non-hydrostatic model (JMA NHM) online coupled with chemistry module Model Resolutions: 2km x 2 km, 50 levels Tropospheric Gas Chemistry: SAPRC-99 chemical mechanism (Carter, 2000) Emission inventories: Anthropogenic : EAGrid-2010 Biogenic VOCs : MEGAN2 Biomass Burning: GFED3 Model domain with terrain height To investigate this point, we performed some numerical simulations by using the regional chemical transport model developed at Meteorological Research Institute, Japan, called NHM-Chem. The chemistry module of this model is coupled online with JMA NHM. The model domain covers Tokyo and the suburban areas with the horizontal resolution of 2 km. The SAPRC99 gas-phase chemical mechanism and these emission inventory data were used in the simulations.

Anthropogenic heating Simulations ・ URBAN simulation: - SPUC urban canopy scheme (Aoyagi and Seino, 2011) - Anthropogenic Heating (Senoo et al., 2004) ・ NO_URB simulation: - Urban land use is replaced with Grasslands - NO Anthropogenic Heating The SPUC scheme was applied on those grids with more than 80% of the land use designated as urban building area, road area, and bare urban area. Here is an outline of the numerical simulations: We performed two simulations for the period of July-August 2010 by using the NHM-Chem. In the URBAN simulation, the SPUC urban canopy scheme was applied in order to take account of surface energy balance in the built area. This scheme was applied only on those grids with more than 80% of the land use designated as urban building area, road area, and bare urban area. The anthropogenic heating, as shown in the right bottom figure, was also considered in this simulation. In the NO_URBAN simulation, urban land use was replaced with grasslands, and there was no anthropogenic heating. Simulation Period: July-August 2010 Anthropogenic heating

Emissions NOx emission Monoterpene emission suburban urban These figures show monthly averaged NOx and Monoterpene emissions in July 2010, which were used as boundary conditions in the two simulations. As you can see, the anthropogenic emission of NOx is large around the Tokyo bay, where is the largest industrialized area in Japan. On the other hand, biogenic VOC emissions such as isoprenes and terpenes are large in rural areas, which surround urban and suburban areas. The red and green rectangles respectively indicate the urban analysis area and the suburban analysis area in this study. Unit is ㎍/m2/sec.

Results: Diurnal variation of NOx in urban area NOx (ppbv) Diff. in PBL Height (m) (URBAN) – (NO_URB) Now, I'd like to look at results of the two simulations: The left figure shows the diurnal variations of the surface NOx volume mixing ratios in July and August, which are averaged over the urban analysis area. The black line indicates the observed diurnal variation, while the red and blue lines respectively indicate those in the URBAN simulation and the NO_URBAN simulation. Compared to the observations, NOx concentration in the NO_URBAN simulation is higher during nighttime, especially at 6 local time. On the other hand, in the URBAN simulation, the diurnal variation is rather realistically reproduced. In the URBAN simulation, the deeper urban boundary layer, as shown in the right figure, leads to the lower NO2 surface concentration, especially in the early morning. 02-04 JST JST 6 12 18 24 Black:OBS Red :URBAN simulation Brue :NO_URB simulation July-August average

Difference between two runs Difference in the surface NO2 concentration Difference between two runs (URBAN) – (NO_URB) NO2 (ppbv) NO2 RMS Error (ppbv) These figures show differences in the surface NO2 concentration and the root mean square errors between two simulations. The lower NO2 concentrations are simulated not only in the urban area but also in the suburban areas. This suggests that not only the enhanced turbulence but also the urban-induced local circulation contribute to the lower concentrations. The root mean square errors in the URBAN simulation are reduced at the greater part of the observation sites. This means that the URBAN simulation captures better the observed surface NO2 distributions.

Diurnal variation of O3 in urban O3 (ppbv) You see here the diurnal variations of the surface O3 volume mixing ratios averaged over the urban analysis area. On average, ozone concentration is 8 ppb higher during the nighttime in the URBAN simulation than in the NO-URBAN simulation. The negative bias during the night time in the NO-URBAN simulation is not seen in the URBAN-simulation. The higher O3 concentration during the night-time is primarily due to the reduced O3 destruction by NO in the deeper urban boundary layer. In the deep urban boundary layer, the primary pollutants such as NO and CO are diluted and hence their near-surface concentrations become lower. The maximum ozone concentration during the daytime is also higher by about 3 ppbv in the URBAN simulation. JST 6 12 18 24 Black:OBS Red :URBAN simulation Brue :NO_URB simulation July-August average

High O3 event in Tokyo area Time series of O3 in July 2010 Now, we focus on a summertime high ozone event in Tokyo. The left figure shows the time series of the observed surface ozone concentrations averaged over the urban analysis area. As you can see, a high ozone event occurred during the period of 20 July to 24 July of 2010. In the weather chart on 21 July, the Pacific high-pressure system extended over the main island of Japan. The Pacific-high remained during this period. Many parts of Japan as well as the Tokyo area experienced significant high temperatures due to the predominant sunny conditions during this period. 20-24 July 21 July, 2010

Temperature and sea-breeze in Tokyo at 15 JST 20-24 July average A B Surface Temperature and Winds Diff. (URBAN) – (NO_URB) B A The simulated surface temperature and winds at 15 local time during this period is shown in the left figure. In the right figure, their differences between the URBAN simulation and the NO_URBAN simulation are shown. Blue arrows in the left figure indicate dominant wind directions. The intrusion of sea-breeze from the south is deep over the land. This sea-breeze basically weakens due to the urban-induced local circulation anomaly, as seen in the right figure. The urban-induced local circulation anomaly is basically characterized by convergent flow anomaly toward the city center in the lower boundary layer. Green arrows indicate this convergent flow anomaly. Next, vertical cross sections from A to B will be shown. A

Vertical cross sections (ave. over 20-24 July) Diff. in PT and (V, W) (URBAN) – (NO_URB) Z (m) A B Diff. in PO3 (=O3+NO2) Z (m) In the upper figure, color shade indicates the vertical cross sections of difference in the potential temperature between the URBAN simulation and the NO_URBAN simulation. Green arrows indicate convergent flow anomaly in the lower boundary layer and divergent flow anomaly toward the surroundings in the upper boundary layer. Due to the urban-induced local circulation anomaly, the potential ozone, which is defined ozone plus NO2, increases in the urban area in the lower boundary layer, while it decreases in the suburban area. The increased potential ozone near the surface in the urban area is effectively transported upward, leading to much higher potential ozone concentration in the upper boundary layer. A B

Maximum 8h O3 concentration 20-24 July average Max 8-hr O3 (ppbv) Diff. in max 8-hr O3 B The horizontal distribution of maximum 8 hour ozone concentration is also influenced by this urban-induced local circulation anomaly. In the URBAN simulation, higher and lower maximum 8 hour ozone concentrations are simulated over the urban area and the suburban area, respectively. A

Diurnal variation of O3 (ave. over 20-24 July) suburban O3 (ppbv) Diurnal variation of the surface ozone concentration averaged over the suburban analysis area is significantly different between the two simulations. Although the daytime ozone concentration in both the two simulations are positively biased, the positive bias in the URBAN simulation is significantly small compared to the NO_URBAN simulation. JST 6 12 18 24 Black:OBS Red :URBAN simulation Brue :NO_URB simulation

Conclusion The deeper urban boundary layer and the UHI circulation decrease surface NOx concentration in the urban and suburban areas, especially in the early morning. The maximum 8h O3 increases in the urban area and decreases in the suburban area due to UHI circulation. Explicit consideration of urban canopy may be important for improving air quality forecasts in suburban areas as well as urban areas. Here is a conclusion of this study: Impacts of urban heat-island circulation on distributions of O3 and NOx in Tokyo during the summer season are investigated by using NHM-Chem. The deeper urban boundary layer and the urban heat island circulation decrease surface NOx concentration in the urban and suburban areas, especially in the early morning. The maximum 8h O3 increases in the urban area and decreases in the suburban area due to urban heat island circulation. Explicit consideration of urban canopy may be important for improving air quality forecasts in suburban areas as well as urban areas.

Thank you for your attention! Acknowledgement This research was partly supported by MEXT as “Priority Issue on Post-K computer” . That’s all. Thank you very much for your kind attention.