Comprehensive Analyses Of Source Sensitivities And .
Atmos. Chem. Phys., 20, 10311–10329, 2020https://doi.org/10.5194/acp-20-10311-2020 Author(s) 2020. This work is distributed underthe Creative Commons Attribution 4.0 License.Comprehensive analyses of source sensitivities and apportionmentsof PM2.5 and ozone over Japan via multiple numerical techniquesSatoru Chatani1 , Hikari Shimadera2 , Syuichi Itahashi3 , and Kazuyo Yamaji41 NationalInstitute for Environmental Studies, Tsukuba, Ibaraki 305-8506, JapanSchool of Engineering, Osaka University, Suita, Osaka 565-0871, Japan3 Central Research Institute of Electric Power Industry, Abiko, Chiba 270-1194, Japan4 Graduate School of Maritime Sciences, Kobe University, Kobe, Hyogo 658-0022, Japan2 GraduateCorrespondence: Satoru Chatani (chatani.satoru@nies.go.jp)Received: 10 March 2020 – Discussion started: 20 April 2020Revised: 30 June 2020 – Accepted: 27 July 2020 – Published: 4 September 2020Abstract. Source sensitivity and source apportionment aretwo major indicators representing source–receptor relationships, which serve as essential information when considering effective strategies to accomplish improved air quality.This study evaluated source sensitivities and apportionmentsof ambient ozone and PM2.5 concentrations over Japan withmultiple numerical techniques embedded in regional chemical transport models, including a brute-force method (BFM),a high-order decoupled direct method (HDDM), and an integrated source apportionment method (ISAM), to updatethe source–receptor relationships considering stringent emission controls recently implemented in Japan and surrounding countries. We also attempted to understand the differences among source sensitivities and source apportionmentscalculated by multiple techniques. While a part of ozoneconcentrations was apportioned to domestic sources, theirsensitivities were small or even negative; ozone concentrations were exclusively sensitive to transport from outsideJapan. Although the simulated PM2.5 concentrations weresignificantly lower than those reported by previous studies,their sensitivity to transport from outside Japan was still relatively large, implying that there has been a reduction inJapanese emissions, similar to surrounding countries including China, due to implementation of stringent emission controls. HDDM allowed us to understand the importance ofthe non-linear responses of PM2.5 concentrations to precursor emissions. Apportionments derived by ISAM were useful in distinguishing various direct and indirect influences onozone and PM2.5 concentrations by combining with sensitivities. The results indicate that ozone transported from outsideJapan plays a key role in exerting various indirect influenceson the formation of ozone and secondary PM2.5 components.While the sensitivities come closer to the apportionmentswhen perturbations in emissions are larger in highly nonlinear relationships – including those between NH3 emis sions and NH 4 concentrations, NOx emissions and NO3concentrations, and NOx emissions and ozone concentrations – the sensitivities did not reach the apportionments because there were various indirect influences including othersectors, complex photochemical reactions, and gas–aerosolpartitioning. It is essential to consider non-linear influencesto derive strategies for effectively suppressing concentrationsof secondary pollutants.1IntroductionThe air quality of Japan has gradually improved. However,ambient concentrations of fine particulate matter smallerthan 2.5 µm (PM2.5 ) and photochemical oxidants (predominantly ozone) exceed the environmental quality standards(EQS). Therefore, we must develop effective strategies tosuppress ambient PM2.5 and ozone concentrations. Quantitative source–receptor relationships serve as essential information when considering effective strategies. There aretwo major indicators representing source–receptor relationships (Clappier et al., 2017). One is source sensitivity, whichcorresponds to a change in ambient pollutant concentrations caused by a certain perturbation in precursor emissions.The second is source apportionment, which corresponds toPublished by Copernicus Publications on behalf of the European Geosciences Union.
10312S. Chatani et al.: Comprehensive analyses of source sensitivities and apportionments over Japanthe contribution of precursor emissions to ambient pollutant concentrations. Receptor modelling, including chemicalmass balance (CMB) and positive matrix factorization (PMF)methods, has been widely applied to evaluate source apportionments (Hopke, 2016). However, these methods have limitations when attempting to treat secondary pollutants, whichform in the atmosphere via complex photochemical reactions. Moreover, receptor modelling cannot evaluate sourcesensitivities. Forward modelling using a regional chemicaltransport model is a powerful tool for evaluating both thesource sensitivities and apportionments of primary and secondary pollutants.Several numerical techniques have been developed for regional transport models to evaluate source sensitivities andapportionments (Dunker et al., 2002; Cohan and Napelenok,2011). A simple technique for evaluating source sensitivitiesis the brute-force method (BFM). Differences in the simulated pollutant concentrations between two simulation caseswith and without perturbations in the input precursor emissions are considered to be the sensitivity to a given emissionsource based on the BFM. This technique can require significant computational resources when evaluating the sensitivities to many emission sources. A decoupled direct method(DDM) is a numerical technique that simultaneously tracksthe evolution of sensitivity coefficients, in addition to pollutant concentrations, when solving model equations (Yanget al., 1997). This method has been extended to a high-orderDDM (HDDM) to track high-order sensitivity coefficients(Hakami et al., 2003). The ozone source apportionment technology (OSAT) (Dunker et al., 2002) and particulate mattersource apportionment technology (PSAT) (Wagstrom et al.,2008) are numerical techniques that evaluate the source apportionments of ozone and particulate matter concentrations,respectively, by tagging contributions of precursor emissionsto simulated concentrations. An integrated source apportionment method (ISAM) is a similar numerical technique thatevaluates source apportionments (Kwok et al., 2013). Eachmethod has its strengths and weaknesses, such that it is important to appropriately interpret results that will be used todevelop effective strategies.Source sensitivities and apportionments of ambient pollutant concentrations over Japan have been evaluated usingregional chemical transport models. Chatani et al. (2011)evaluated the sensitivities of simulated PM2.5 concentrationsover three metropolitan areas in Japan to domestic sourcesand transboundary transport in the 2005 fiscal year. Ikedaet al. (2015) evaluated the sensitivities of simulated PM2.5concentrations over the nine receptor regions in Japan tosource regions in Japan, Korea, and China in 2010. These twostudies only employed the BFM to derive source sensitivitiesof PM2.5 concentrations. Itahashi et al. (2015) evaluated thesensitivities and apportionments of simulated ozone concentrations over East Asia to sources in Japan, Korea, and China.That study presented a unique exercise discussing the differences in source sensitivities and apportionments derived byAtmos. Chem. Phys., 20, 10311–10329, 2020multiple techniques, including the BFM, HDDM, and OSAT,in Asia; these differences have only been discussed in limited studies targeting the United States and Europe (Kooet al., 2009; Burr and Zhang, 2011; Thunis et al., 2019). Expanding targets is key to obtaining a more comprehensiveand appropriate understanding of the source sensitivities andapportionments of pollutant concentrations, including ozoneand PM2.5 , across Asia, including Japan, derived by multipletechniques.In addition, recent studies (van der A et al., 2017; Wanget al., 2017; Zheng et al., 2018) suggest that stringent emission controls implemented in China have achieved improvedair quality. These improvements should affect air quality notonly in China but also across downwind regions includingJapan. We must, therefore, update source sensitivities andapportionments when considering additional effective strategies aimed at further air quality improvement in Japan.Mutual inter-comparisons of the source sensitivities andapportionments derived by multiple models and numerical techniques is one of the objectives of Japan’s Studyfor Reference Air Quality Modelling (J-STREAM) (Chataniet al., 2018b). Model inter-comparisons conducted in earlierphases of J-STREAM have contributed to the derivation ofmodel configurations and development of emission inventories, both of which have contributed to improved model performance (Chatani et al., 2020; Yamaji et al., 2020). As oneof the subsequent activities of J-STREAM, this study evaluates the source sensitivities of ozone and PM2.5 concentrations simulated over regions in Japan for a recent year using the outcomes obtained in earlier phases of J-STREAM.Comprehensive analyses from various perspectives were performed to evaluate the sensitivities to eight domestic andtwo natural emission source groups, as well as foreign anthropogenic emission sources and transboundary transportthroughout the entire 2016 fiscal year. In addition, we perform mutual comparisons of the source sensitivities and apportionments of simulated ozone and PM2.5 concentrations.Although the target periods were limited to 2 weeks in fourseasons, we discuss notable characteristics with respect tothe differences in the source sensitivities and apportionmentsderived by the BFM, HDDM, and ISAM.There are well-known non-linear relationships betweenambient concentrations of secondary pollutants includingozone and secondary components involved in PM2.5 (Seinfeld and Pandis, 1998). They are likely to cause deviationsbetween source sensitivities and apportionments due to complex photochemical reactions and gas–aerosol partitioning.Nevertheless, it is important to investigate magnitudes ofdeviations and major causes of non-linear relationships forconsidering effective strategies to suppress concentrations ofsecondary pollutants. Processes causing non-linear relationships are universal phenomena and not limited to Japan. Thefindings of this study contribute not only to solving remaining issues involving ozone and PM2.5 in Japan, but also tohttps://doi.org/10.5194/acp-20-10311-2020
S. Chatani et al.: Comprehensive analyses of source sensitivities and apportionments over Japanunderstanding of possible influences of non-linear relationships in other countries and regions.22.1MethodologyModel configurationThe Community Multiscale Air Quality (CMAQ) modellingsystem (Byun and Schere, 2006) version 5.0.2, in which boththe HDDM and ISAM are embedded, was selected to calculate the source sensitivities and apportionments, in additionto ambient pollutant concentrations. The carbon bond chemical mechanism with the updated toluene chemistry (CB05TU) (Whitten et al., 2010) and aero6 aerosol module wereemployed. Input meteorological fields were simulated by theWeather Research and Forecasting (WRF) – Advanced Research WRF (ARW) version 3.7.1 (Skamarock et al., 2008).Horizontal locations and resolutions of the four target domains, named d01, d02, d03, and d04, remain unchangedsince the first phase of J-STREAM (Chatani et al., 2018b), asshown in Fig. 1. Horizontal resolutions of d01, d02, d03, andd04 are 45 45, 15 15, 5 5, and 5 5 km, respectively.The top height of the model was lifted from 10 000 to 5000 Pato explicitly treat transport in the lower stratosphere (Itahashiet al., 2020). The vertical layer heights were adjusted to beconsistent with those of the Chemical Atmospheric GlobalClimate Model for Studies of Atmospheric Environment andRadiative Forcing (CHASER) (Sudo et al., 2002), which wasused to provide boundary concentrations, to avoid numericaldiffusions to adjacent layers. Each vertical layer of CHASERfrom the ground to 80 000 Pa was further divided into two tosimulate vertical variations in the lower atmosphere in moredetail. The bottom layer height was approximately 28 m.Several changes were applied to the original WRF configuration employed in the first phase of J-STREAM described in Chatani et al. (2018b) based on the outcomesof the model inter-comparisons. The input land use datasetwas replaced with one created from geographic informationsystem (GIS) data based on the sixth and seventh vegetation surveys released by the Biodiversity Centre of Japan,Ministry of Environment, which yielded improved performance for multiple meteorological parameters over urban areas (Chatani et al., 2018a). Lakes were added to the datasetbased on the National Land Numerical Information lakesdata. The shortwave and longwave radiation schemes werereplaced with the Rapid Radiative Transfer Model for General Circulation Models (RRTMG) schemes (Iacono et al.,2008) to use the climatological ozone and aerosol profileswith spatial, temporal, and compositional variations (Tegenet al., 1997). Microphysics and cumulus schemes had significant influences on the simulated pollutant concentrationsin the model inter-comparisons. A Morrison double-momentmicrophysics scheme (Morrison et al., 2009) and Grell–Devenyi ensemble cumulus scheme (Grell and 03132002) were newly selected because they were characterizedby better performance during the sensitivity experiments.Analysis datasets were replaced with the finer ones, i.e. theNCEP GDAS/FNL 0.25 Degree Global Tropospheric Analyses and Forecast Grids (ds083.3) (National Centers for Environmental Prediction/National Weather Service/NOAA/U.S.Department of Commerce, 2015) and Group for High Resolution Sea Surface Temperature (GHRSST) (Martin et al.,2012), for the initial and boundary conditions, as well asgrid nudging. Nudging coefficients are critical parametersfor model performance (Spero et al., 2018), but forcingterms in the model equations may disturb physical consistencies. While nudging coefficients for winds were set to1.0 10 4 s 1 for all domains and vertical layers, those fortemperature and water vapour were reduced to 5.0 10 5 ,3.0 10 5 , 1.0 10 5 , and 1.0 10 5 s 1 for d01, d02,d03, and d04, respectively. In addition, nudging for the temperature and water vapour within the planetary boundarylayer in d03 and d04 was turned off to avoid excessive nudging to finer spatial and temporal scales than the input analysisdatasets, as well as to allow the simulated values to be in accordance with the physical equations.2.2Emission inputsVarious improvements were applied to the original emissioninputs used in the first phase of J-STREAM described inChatani et al. (2018b) based on the outcomes of the modelinter-comparisons. Hemispheric Transport of Air Pollution(HTAP) emissions version 2.2 (Janssens-Maenhout et al.,2015) was used for anthropogenic sources and internationalshipping for Asian countries except for Japan. While the target year of HTAP v2.2 is 2010, the ratios of sectoral annualemissions reported by Zheng et al. (2018) were multiplied forChina, and those reported by the Clean Air Policy SupportSystem (CAPSS) (Lee et al., 2011) were multiplied for SouthKorea, to represent the changes in the precursor emissions ofrecent years. Itahashi et al. (2018) suggested the importanceof heterogeneous reactions involving Fe and Mn in sulfateformation. The speciation profiles of Fu et al. (2013) wereapplied to consider other components, including Fe and Mn,in addition to originally available black and organic carbon inPM2.5 emissions. The PM2.5 emission inventory developedby the Ministry of Environment for the 2015 fiscal year wasused for on-road and other transportation sectors in Japan.Emissions from stationary sources in Japan developed in JSTREAM (Chatani et al., 2018b) were fully updated to the2015 fiscal year with the following improvements. The emission database of large point sources discretized into sectors,facilities, and fuel types was newly developed by Chataniet al. (2019) based on research of air pollutant emissionsfrom stationary sources to represent emissions characteristicsand speciation profiles including Fe and Mn. Missing fugitive volatile organic compound (VOC) emission sources, including the use of repellents, air fresheners, aerosol inhalers,Atmos. Chem. Phys., 20, 10311–10329, 2020
10314S. Chatani et al.: Comprehensive analyses of source sensitivities and apportionments over JapanFigure 1. Target domains for the simulations in this study. Results are summarized for six colour-coded regions in d02 and three designatedareas shown in red in d02, d03, and d04. Their abbreviations are shown in parentheses.cosmetic products, and products for car washing and repair,were added to be consistent with the Greenhouse Gas Inventory Office of Japan (2018). NH3 emissions from fertilizeruse and manure management were replaced by the valuesreported by the Greenhouse Gas Inventory Office of Japan(2018). Fugitive VOC and PM emissions from manure management were newly estimated based on the European Environment Agency (2016). Emission factors of other NH3sources, including human sweat, human breath, dogs, andcats, were replaced by those reported in Sutton et al. (2000).PM emissions from the abrasion of railways wires and railswere newly estimated as one of the major sources of Feand Mn. The method to estimate emissions from open agricultural residue burning were replaced by that used by theGreenhouse Gas Inventory Office of Japan (2018). We applied the emission factors reported in Fushimi et al. (2017)and Hayashi et al. (2014), as well as the temporal variationsfrom Tomiyama et al. (2017). Biogenic VOC emissions wereestimated by Chatani et al. (2018a) using a detailed databaseof vegetation and emission factors specific to Japan. The surfzone, defined as zones adjacent to beaches in the NationalLand Numerical Information Land Use Fragmented MeshAtmos. Chem. Phys., 20, 10311–10329, 2020Data, was newly added to estimate higher sea salt emissionsfrom these areas (Gantt et al., 2015) in the CMAQ.2.3Simulation setupAmbient pollutant concentrations in d01, d02, d03, and d04were simulated for the entire 2016 fiscal year (from April2016 to March 2017). Simulations for the preceding month(March 2016) were treated as spin-up. Sensitivities to theemission source groups, classified as listed in Table 1, wereevaluated by the BFM, in which the emissions of each sourcegroup were reduced by 20 % for the entire fiscal year in d02and 2 selected weeks in spring (from 6 to 20 May), summer (from 21 July to 4 August), autumn (from 20 Octoberto 3 November), and winter (from 19 January to 2 February2017) in d03 and d04. These 2 weeks in the four seasons werethe periods in which the monitoring campaigns for the ambient concentrations of the PM2.5 components were conductedthroughout Japan. The reason for choosing 20 % reductionas a perturbation range in BFM is that it is a typical range ofemission reduction by potential emission controls. For s11(transport through the boundaries of d02), the boundary concentrations of all species for d02 were reduced by 20 %. Difhttps://doi.org/10.5194/acp-20-10311-2020
S. Chatani et al.: Comprehensive analyses of source sensitivities and apportionments over Japanferences in the concentrations scaled by 5 between the simulations with and without 20 % perturbations were treatedas sensitivities in this study. In addition, source sensitivitiesand apportionments to all the emission source groups listedin Table 1 were evaluated by the HDDM and ISAM, respectively, using consistent inputs for the 2 coincident weeks inthe four seasons in d02. The first- and second-order sensitivity coefficients to gaseous precursors of a single emissionso
Several numerical techniques have been developed for re-gional transport models to evaluate source sensitivities and apportionments (Dunker et al., 2002; Cohan and Napelenok, 2011). A simple technique for evaluating source sensitivities is the brute-force method (BFM). Differences in the simu-lated pollutant concentrations between two .
Tests for Paired Sensitivities Introduction This procedure gives power or required sample size for comparing two diagnostic test s when the outcome is sensitivity. In the design covered in this chapter, the sensitivities of two diagnostic tests are each performed on the same subject.
The calculation of these sensitivities involve two terms: analysis terms that are encountered during the usual nite element analysis and isogeometric analy-sis and geometric sensitivities that are represented as the derivatives of positions and weights over design variables. Mesh re nement is often required for accu-rate nite element analysis.
the Source 1 power source until the Source 2 power source does appear. Conversely, if connected to the Source 2 power source and the Source 2 power source fails while the Source 1 power source is still unavailable, the ATS remains connected to the Source 2 power source. ATSs automatically perform the transfer function and include three basic .
Dynamic analyses can generate "dynamic program invariants", i.e., invariants of observed execution; static analyses can check them Dynamic analyses consider only feasible paths (but may not consider all paths); static analyses consider all paths (but may include infeasble paths) Scope Dynamic analyses examine one very long program path
1. INTRODUCTION. 2. SUMMARY OF MODEL. 3. SUMMARY OF RESULTS. 3.1 Base case and non-UPI. 3.2 Coverage sensitivities. 4. CONCLUSIONS. 5. REFERENCES. 6. LIST OF TABLES AND FIGURES. ) 6.1 Base case and non-UPI. 6.2 Coverage sensitivities. 7. FIGURES AND TABLES. A 8302280312 8
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7. Calculate the theoretical static and dynamic balance sensitivities (See calculations that follow procedure.) 8. Calculate both the static and dynamic masses required at each balance plane from the sensitivities. 9. Determine the angles by which the high spots lag the heavy spots from the two coast down plots. Locate
Abstract- Abrasive Water Jet Machining (AWJM) is a versatile machining process primarily used to machine hard and difficult to machine materials. The objective of this paper is to optimize material removal rate and kerf width simultaneously using AWJM process on INCONEL 718. The process parameters are chosen as abrasive flow rate, pressure, and standoff distance. Taguchi Grey Relational .
