Trends in PM 2.5 Concentration in Nagoya, Japan, from 2003 to 2018 and Impacts of PM 2.5 Countermeasures

: In Japan, various countermeasures have been undertaken to reduce the atmospheric concentration of ﬁne particulate matter (PM 2.5 ). We evaluated the extent to which these countermeasures were effective in reducing PM 2.5 concentrations by analyzing the long-term concentration trends of the major components of PM 2.5 and their emissions in Nagoya City. PM 2.5 concentrations decreased by 53% over the 16-year period from ﬁscal years 2003 to 2018 in Nagoya City. Elemental carbon (EC) was the component of PM 2.5 with the greatest decrease in concentration over the 16 years, decreasing by 4.3 µ g/m 3 , followed by SO 42 − (3.0 µ g/m 3 ), organic carbon (OC) (2.0 µ g/m 3 ), NH 4+ (1.6 µ g/m 3 ), and NO 3 − (1.3 µ g/m 3 ). The decrease in EC concentration was found to be caused largely by the effect of diesel emission control. OC concentrations decreased because of the effects of volatile organic compound (VOC) emission regulations for stationary sources and reductions in VOCs emitted by vehicles and construction machinery. NO 3 − concentrations decreased alongside decreased contributions from vehicles, construction machinery, and stationary sources, in descending order of the magnitude of decrease. Although these ﬁndings identify some source control measures that have been effective in reducing PM 2.5 , they also reveal the ineffectiveness of some recent countermeasures for various components, such as those targeting OC concentrations. oxidized from biogenic VOCs and unidentiﬁed anthropogenic VOCs, and long-range transport. Biogenic VOC emissions in Japan were estimated to be 1303 Gg/year in 2016 [41], which was higher than anthropogenic VOC emissions (671 Gg/year) [42]. To further reduce OC concentrations, these source contributions need to be clariﬁed. and H.N.; resources, K.U.; data curation, M.Y.; writing—original draft M.Y.; writing—review F.I., and S.W.; visualization, M.Y.


Introduction
Atmospheric aerosol particles are solid particles or liquid droplets of various sizes suspended in the air. Among aerosol particles, fine particles can be inhaled by the human body [1]; fine particles less than 2.5 µm in diameter are defined collectively as PM 2.5 . PM 2.5 can reach deep into the lungs and is known to cause health problems [2]. In Japan, the atmospheric PM 2.5 concentration has a strong effect on mortality [3,4]. PM 2.5 is composed of a variety of chemicals, including carbonaceous components, such as elemental carbon (EC) and organic carbon (OC), and inorganic components, such as sulfate and nitrate as major components. EC is produced by the incomplete combustion of fossil fuels, such as oil and coal, and of biomass, such as wood. OC is produced by the combustion of fossil fuels and biomass and by the photooxidation of volatile organic compounds (VOCs) in the atmosphere. These include anthropogenic VOCs used in paints, printing inks, adhesives, and cleaning agents, as well as biogenic VOCs, such as isoprene released from plant leaves. Sulfate is formed by the oxidation of sulfur dioxide (SO 2 ), which is produced by the combustion of fossil fuels; some SO 2 is also released from volcanoes. Nitrate is formed by the oxidation of nitrogen oxides (NO X ), which are produced by the combustion of fuels. To (national route 23), was located 110 m south of the NCIES. These aspects and components of the surrounding environment did not change during the study period.

Chemical Analyses
The procedure for analyzing PM2.5 was the same as that used in Yamagami et al. [11]. After weighing for the PM2.5 mass concentration, the PTFE filters were cut into halves for additional chemical analyses. One half was placed in a pre-cleaned polypropylene tube (15 mL; Iwaki Glass Co. Ltd., Shizuoka, Japan) with 10 mL ultrapure water (18.2 MΩ,   (Figure 1). The sampling site was surrounded by a busy industrial area and a residential area. One possible strong EC source, a busy road (national route 23), was located 110 m south of the NCIES. These aspects and components of the surrounding environment did not change during the study period.
We collected PM 2.5 samples through an EPA well-type impactor ninety-six (WINS; [10]) inlet at a flow rate of 16 Whatman, Maidstone, U.K.) and a quartz-fiber filter (47 mm diameter, 2500 QAT-UP; Pall Corp., New York, U.S.). PTFE filters were used for analyzing mass concentrations of PM 2.5 and major ions. The PTFE filters were weighed before and after sampling using a microbalance (ME5-F; Sartorius AG, Göttingen, Germany) under constant temperature (21.5 • C ± 1.5 • C) and relative humidity (35% ± 5%) for 24 h. Quartz-fiber filters were used for analyzing EC and OC.

Chemical Analyses
The procedure for analyzing PM 2.5 was the same as that used in Yamagami et al. [11]. After weighing for the PM 2.5 mass concentration, the PTFE filters were cut into halves for additional chemical analyses. One half was placed in a pre-cleaned polypropylene tube (15 mL; Iwaki Glass Co. Ltd., Shizuoka, Japan) with 10 mL ultrapure water (18.2 MΩ, PURELAB Ultra, ELGA/ORGANO, High Wycombe, U.K.) for 20 min in an ultrasonic bath. After the extract was filtered using a 0.2-µm pore size PTFE filter (DISMIC-25HP; Advantec, Tokyo, Japan), it was analyzed for the major ions (Cl − , NO 3 − , SO 4 2− , Na + , NH 4 + , K + , Mg 2+ , and Ca 2+ ) using an ion chromatograph (ICS-1000; Dionex Corp., Sunnyvale, CA, U.S.). Commercially available standard solutions were used for ionic analyses (Anion Mixture Standard Solution 1 and Multication Standard Solution; Wako Pure Chemical Industries Ltd., Osaka, Japan). EC and OC concentrations were measured in terms of thermal optical reflectance following the IMPROVE protocol [12] using a carbon analyzer (Sunset Laboratory Inc.,New York, NY, USA). The OC fractions were measured at four temperature steps in 100% helium: OC1 at 120 • C, OC2 at 250 • C, OC3 at 450 • C, and OC4 at 550 • C. The EC fractions were measured at three temperature steps in a mixture of 2% oxygen and 98% helium: EC1 at 550 • C, EC2 at 700 • C, and EC3 at 850 • C. The carbon evolved at each temperature was oxidized to carbon dioxide (CO 2 ). The CO 2 was then reduced to methane (CH 4 ); it was then quantified using a flame ionization detector.

Emission Data
For some sources, emissions were estimated every few years. For FYs 2003FYs , 2004FYs , and 2006FYs -2011 which no aggregated data are available, emissions in FYs 2001 [13], 2005 [14], and 2012 [15] were supplemented by linear regression and calculated by scaling the estimate according to the respective parameters described below. Data for FYs 2013-2018, for which no aggregated data are available, were based on the projected emissions for FY 2018, which were calculated by scaling the estimate according to the respective parameters and aggregated.

Mobile Sources EC
The method used to calculate EC emissions from vehicles is described in detail by Yamagami et al [11]. Annual particulate matter emissions from vehicles in the Nagoya urban area were reported by Ministry of Environment, Japan (MOE) [16], based on diesel traffic data and particle emission factors. Traffic data for diesel vehicles were recorded based on the vehicle type, holiday and weekdays, and time zones. Vehicles were classified as cars, buses, light-duty diesel trucks, medium-duty diesel trucks, heavy-duty diesel trucks, or "construction machinery, etc." Note that these emissions were calculated assuming zero particulate matter emissions from gasoline vehicles. According to a report from MOE [17], the ratio of EC in particles emitted from diesel vehicles was 70%. Annual EC emissions in the Nagoya urban area were calculated by multiplying the particle emissions from vehicles by 0.7. VOC VOC emissions from fuel evaporation from vehicles in Nagoya City were calculated based on emission factors and traffic volumes [13][14][15]. For the years for which no data were collected, emissions were calculated by scaling the estimate according to the vehicle traffic [18]. VOC emissions from construction machinery, etc., were calculated based on the total VOC emissions of construction and industrial machinery in Japan via proportional allocation to the city of Nagoya according to the construction cost and the value of manufactured goods shipped [13][14][15]. VOC emissions from agricultural machinery were less than 1 ton (t) per year, so they were not included in the total. Emissions for years with no data were calculated by scaling the estimate according to energy consumption by industry [19]. SO 2 SO 2 emissions from vehicles in Nagoya City were calculated based on the sulfur content of diesel and gasoline, fuel consumption by vehicle type, and traffic volume [15]. Emissions for years for which no data were available were calculated by scaling the estimate according to the sales volume of diesel and gasoline in Aichi Prefecture [20,21] and the sulfur content in diesel and gasoline at the time. NO X NO X emissions from vehicles in Nagoya City were calculated based on emission factors and traffic volumes by vehicle type [13][14][15]. Emissions for years for which no data were available were calculated by scaling the estimate according to the traffic volumes [18]. NO X emissions from construction machinery, etc., were calculated based on the total estimated NO X emissions in Japan via proportional allocation to Nagoya City [13][14][15]. Emissions for years with no available data were calculated by scaling the estimate according to the industrial energy consumption [19].

Stationary Sources VOC
VOC emissions from stationary sources were compiled from data in the Pollutant Release and Transfer Register [22]. The emissions of the top 20 substances with the largest emission masses into the atmosphere in Nagoya City from FYs 2003 to 2018 were tabulated by industry. These 20 substances were acrylonitrile, ethylbenzene, ethylene glycol, ethylene glycol monoethyl ether, propylene oxide, xylene, chloroform, ethylene glycol monoethyl ether acetate, vinyl acetate, dichlorobenzene, methylene chloride, tetrachloroethylene, normal-dodecyl alcohol, trichloroethylene, 1,2,4-trimethylbenzene, 1,3,5-trimethylbenzene, toluene, normal-hexane, benzene, and methyl methacrylate. The emissions of these 20 substances accounted for 97-99% of the total VOC emissions from stationary sources in each year.
Atmosphere 2021, 12, 590 5 of 16 SO 2 SO 2 emissions from large-scale industrial facilities in Nagoya City were calculated annually based on flue gas SO 2 concentrations and either flue gas volume or fuel consumption [20,21,[23][24][25][26][27][28][29][30][31][32][33][34][35][36]. SO 2 emissions from small-scale industrial facilities were calculated based on the fuel consumption of tertiary industries and sulfur content by fuel type [13][14][15]. Emissions for years with no data were calculated by scaling the estimate according to the population of Nagoya City at the time [37]. SO 2 emissions from households were calculated based on kerosene consumption and the sulfur content in kerosene [13][14][15]. Emissions for years with no data were calculated by scaling the estimate according to the number of households in Nagoya City at the time [37]. NO X NO X emissions from large-scale industrial facilities in Nagoya City were calculated annually based on flue gas NO X concentrations and flue gas volumes [20,21,[23][24][25][26][27][28][29][30][31][32][33][34][35][36]. NO X emissions from small-scale industrial facilities were calculated based on the fuel consumption of tertiary industries and NO X emission factors by fuel type [13][14][15]. Emissions in years for which no data were available were calculated by scaling the estimate according to the population of Nagoya City at the time [37]. NO X emissions from households were calculated based on the fuel consumption of households and NO X emission factors for each fuel type [13][14][15]. Emissions for years with no data were calculated by scaling the estimate according to the number of households in Nagoya City at the time [37].

Backward Trajectory Analysis
We calculated the backward trajectories using the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model version 4 [38]. The trajectories were calculated using the model vertical velocity, and the trajectory duration was 5 days. The starting height of the trajectories was set at 1500 m above ground level over NCIES, and the starting time was set at 0300 UTC (1200 JST).   Figure 3b shows EC emissions from diesel vehicles by type, estimated for the Nagoya urban area. Annual EC emissions decreased from 1600 t in FY 2003 to 184 t in FY 2018, a decrease of 88% over 16 years, that is, 6.0%/year on average. The declines in EC concentration and EC emissions over the 16-year period were about the same. EC emissions decreased considerably through FY 2010 and more moderately after FY 2010. The vehicle type with the highest EC emissions was heavy-duty diesel trucks, which accounted for a fairly constant proportion of total EC emissions over the 16-year study period: 76% in FY 2003 and 73% in FY 2018. EC emissions from cars accounted for 6% of total emissions in FY 2003, but dropped to 1% by FY 2018. Figure 4 presents the relationship between annual EC emissions from diesel vehicles and the annual mean EC concentration. A linear regression analysis was conducted with the annual mean concentration as the dependent variable and emissions as the independent variable. These parameters were extremely strongly correlated (r = 0.97) over the period from FYs 2003 to 2018. These high correlations suggest that regulations on diesel vehicle Atmosphere 2021, 12, 590 7 of 16 emissions were effective in reducing EC concentrations. The intercept of the regression line is interpreted to represent the EC concentration from sources other than diesel vehicles. Sources other than diesel vehicles include biomass combustion; combustion of fossil fuels, such as stationary sources and ships; and transboundary pollution. The contribution to EC concentration of these sources was estimated to be 0.24 ± 0.13 µg/m 3 , based on the intercept value. The impact of these sources was relatively small when EC concentrations were high. However, in 2018, when EC concentrations were low, this "other sources" value corresponded to one third of the annual mean EC concentration of 0.69 µg/m 3 ; such EC sources are becoming increasingly influential. In recent years, the influence of sources other than diesel vehicles has begun to be observed at NCIES, as EC concentrations have been observed to increase depending on the wind direction, because of the combustion of heavy oil from ships and stationary sources in the bay [39].   Figure 4 presents the relationship between annual EC emissions from diesel vehicles and the annual mean EC concentration. A linear regression analysis was conducted with the annual mean concentration as the dependent variable and emissions as the independent variable. These parameters were extremely strongly correlated (r = 0.97) over the period from FYs 2003 to 2018. These high correlations suggest that regulations on diesel vehicle emissions were effective in reducing EC concentrations. The intercept of the regression line is interpreted to represent the EC concentration from sources other than diesel vehicles. Sources other than diesel vehicles include biomass combustion; combustion of fossil fuels, such as stationary sources and ships; and transboundary pollution. The contribution to EC concentration of these sources was estimated to be 0.24 ± 0.13 μg/m 3 , based on the intercept value. The impact of these sources was relatively small when EC concentrations were high. However, in 2018, when EC concentrations were low, this "other sources" value corresponded to one third of the annual mean EC concentration of 0.69 μg/m 3 ; such EC sources are becoming increasingly influential. In recent years, the influ- The top five emitters were the manufacture of ceramic, stone, and clay products; transportation equipment; fabricated metal products; non-ferrous metals and products; and food. Together, these five emitters accounted for 70-80% of stationary source emissions. VOC emissions from the manufacture of ceramic, stone, and clay products accounted for 31% (1598 t) of total emissions in FY 2003, but decreased year by year to less than 1% (10 t) in FY 2018. On the other hand, VOC emissions from the manufacture of food increased from FY 2010, accounted for 22% (330 t) of total emissions in FY 2018. In sum, the industryby-industry proportions of emissions from stationary sources changed significantly during the 16-year study period. Figure 6 presents the relationship between the annual VOC emissions and the annual mean OC concentration. These parameters were extremely strongly correlated (r = 0.97) over the period from FYs 2003 to 2018. However, the OC concentration did not decrease after FY 2016 even as VOC emissions decreased. The VOCs that decreased after FY 2016 were found not to contribute to OC production. The intercept of the regression line from FYs 2003 to 2018 is 1.9 ± 0.14 µg/m 3 , which corresponds to 58% of the FY 2018 OC concentration of 3.3 µg/m 3 . This finding indicates that the contribution from sources other than anthropogenic VOC sources was 58%. One such "other source" is biomass burning. The contribution of biomass burning to OC was 40% in autumn in Nagoya City [40]. Other sources include anthropogenic primary OC, condensable particle matter from stationary sources, OC oxidized from biogenic VOCs and unidentified anthropogenic VOCs, and long-range transport. Biogenic VOC emissions in Japan were estimated to be 1303 Gg/year in 2016 [41], which was higher than anthropogenic VOC emissions (671 Gg/year) [42]. To further reduce OC concentrations, these source contributions need to be clarified.

Long-Term Trends in PM 2.5 Concentration and Chemical Composition
osphere 2021, 12, x FOR PEER REVIEW ence of sources other than diesel vehicles has begun to be observed at NCIES centrations have been observed to increase depending on the wind direction the combustion of heavy oil from ships and stationary sources in the bay [39] Figure 6 presents the relationship between the annual VOC emissions and the annual mean OC concentration. These parameters were extremely strongly correlated (r = 0.97) over the period from FYs 2003 to 2018. However, the OC concentration did not decrease after FY 2016 even as VOC emissions decreased. The VOCs that decreased after FY 2016 were found not to contribute to OC production. The intercept of the regression line from FYs 2003 to 2018 is 1.9 ± 0.14 μg/m 3 , which corresponds to 58% of the FY 2018 OC concentration of 3.3 μg/m 3 . This finding indicates that the contribution from sources other than anthropogenic VOC sources was 58%. One such "other source" is biomass burning. The contribution of biomass burning to OC was 40% in autumn in Nagoya City [40]. Other sources include anthropogenic primary OC, condensable particle matter from stationary sources, OC oxidized from biogenic VOCs and unidentified anthropogenic VOCs, and long-range transport. Biogenic VOC emissions in Japan were estimated to be 1303 Gg/year in 2016 [41], which was higher than anthropogenic VOC emissions (671 Gg/year) [42]. To further reduce OC concentrations, these source contributions need to be clarified.    Figure 8 presents the relationship between annual SO2 emissions and the annual mean SO4 2− concentration. Overall, the SO4 2− concentration was lower at lower SO2 emissions levels (r = 0.85), but the association between SO4 2− concentration and SO2 emissions prior to FY 2008 was weak (r = 0.60, n = 5). Moreover, a negligible association was found between the SO4 2− concentration and SO2 emissions after FY 2008. The intercept of the regression line assumed a high value of 2.7 ± 0.29 μg/m 3 . This value was almost as high as the annual mean value in FY 2018, suggesting a large contribution from sources other than SO2 emissions in Nagoya City. SO4 2− concentrations in Japan are known to be affected by transboundary pollution [9]. SO2 emissions in China peaked in 2006 and then showed a moderate downward trend through 2015, followed by a significant single-year decrease of 40% in 2016 [43]. SO4 2− concentrations in Nagoya also showed a downward trend through 2015 and may have been influenced by emissions originating in China. However, there was no peak in China's SO2 emissions in FY 2013 to correspond to the 2013 peak in the SO4 2− concentration at the NCIES. In addition, the single-year 13% decrease in SO4 2− concentration in FY 2016 was not as large as the decrease in China's SO2 emissions. Thus, SO4 2− concentrations in Nagoya and emissions in China did not match particularly well from year to year. The impact of transboundary pollution on the SO4 2− concentration is expected to be related largely to meteorological factors.
In Japan, SO2 emissions from volcanoes have been observed frequently, and SO4 2− measurement values at observation sites are affected by the wind direction. Because the observation site is close to the ocean, it may be affected by dimethyl sulfide (DMS) emitted from the ocean. Phytoplankton in the ocean are more active in summer, and SO4 2− derived from DMS is expected to affect them in summer when there are stronger southerly winds  Figure 7b shows SO 2 emissions in Nagoya City. Annual SO 2 emissions decreased from 504 t in FY 2003 to 148 t in FY 2018, a decrease of 71% over 16 years. As in the case of OC, the reduction in SO 4 2− concentration (54%) was smaller than the reduction in SO 2 emissions. SO 2 emissions decreased gradually beginning in FY 2003, but dropped significantly in FY 2008 and remained low thereafter. In particular, SO 2 emissions from vehicles decreased significantly in FYs 2007 and 2008. This drop was due to the tightening of regulations on sulfur content in diesel oil from 50 to 10 ppm in 2007 and in gasoline from 50 to 10 ppm in 2008. SO 2 emissions from stationary sources also decreased significantly in FY 2008. This drop was due to a significant decrease in fuel consumption of heavy oil at stationary sources in Nagoya City and a switch to LNG. However, the SO 4 2− concentration did not decrease significantly in FY 2008. Figure 8 presents the relationship between annual SO 2 emissions and the annual mean SO 4 2− concentration. Overall, the SO 4 2− concentration was lower at lower SO 2 emissions levels (r = 0.85), but the association between SO 4 2− concentration and SO 2 emissions prior to FY 2008 was weak (r = 0.60, n = 5). Moreover, a negligible association was found between the SO 4 2− concentration and SO 2 emissions after FY 2008. The intercept of the regression line assumed a high value of 2.7 ± 0.29 µg/m 3 . This value was almost as high as the annual mean value in FY 2018, suggesting a large contribution from sources other than SO 2 emissions in Nagoya City. SO 4 2− concentrations in Japan are known to be affected by transboundary pollution [9]. SO 2 emissions in China peaked in 2006 and then showed a moderate downward trend through 2015, followed by a significant single-year decrease of 40% in 2016 [43]. SO 4 2− concentrations in Nagoya also showed a downward trend through 2015 and may have been influenced by emissions originating in China. However, there was no peak in China's SO 2 emissions in FY 2013 to correspond to the 2013 peak in the SO 4 2− concentration at the NCIES. In addition, the single-year 13% decrease in SO   Figure 7, the same decreasing trend is observed in all seasons (spring, summer, autumn, and winter), suggesting that changes in factors other than DMS contributed to the decreasing trend. SO 4 2− concentrations in Nagoya City may have been affected substantially by transboundary pollution, volcanoes, and meteorological factors, rather than primarily by sources in Nagoya City.  Figure 10 presents the relationship between annual NOX emissions and the annual mean NO3 − concentrations. The NO3 − concentration was lower at lower NOX emissions levels (r = 0.87), indicating that the reduction in NOX emissions in Nagoya City was effective in reducing nitrate in PM2.5. On the other hand, the intercept of the regression line was negative. This finding indicates that particulate NO3 − would not be observed for NOX emissions below 6100 t, assuming that the linear relationship is maintained as NOX emissions decrease. As NOX emissions continue to decrease in the future, it will be necessary to closely monitor changes in the concentration of NO3 − in PM2.5.    Figure 10 presents the relationship between annual NO X emissions and the annual mean NO 3 − concentrations. The NO 3 − concentration was lower at lower NO X emissions levels (r = 0.87), indicating that the reduction in NO X emissions in Nagoya City was effective in reducing nitrate in PM 2.5 . On the other hand, the intercept of the regression line was negative. This finding indicates that particulate NO 3 − would not be observed for NO X emissions below 6100 t, assuming that the linear relationship is maintained as NO X emissions decrease. As NO X emissions continue to decrease in the future, it will be necessary to closely monitor changes in the concentration of NO 3 − in PM 2.5 .
3.6. Ammonium (NH 4 + ) in PM 2.5 NH 4 + in PM 2.5 is typically present as ammonium sulfate or ammonium nitrate. Figure 11 presents the relationship between the sum of SO 4 2− Figure 10 presents the relationship between annual NOX emissions and mean NO3 − concentrations. The NO3 − concentration was lower at lower NO levels (r = 0.87), indicating that the reduction in NOX emissions in Nagoya Cit tive in reducing nitrate in PM2.5. On the other hand, the intercept of the regress negative. This finding indicates that particulate NO3 − would not be observ emissions below 6100 t, assuming that the linear relationship is maintained a sions decrease. As NOX emissions continue to decrease in the future, it will b to closely monitor changes in the concentration of NO3 − in PM2.5.  Atmosphere 2021, 12, x FOR PEER REVIEW 3.6. Ammonium (NH4 + ) in PM2.5 NH4 + in PM2.5 is typically present as ammonium sulfate or ammonium ni 11 presents the relationship between the sum of SO4 2− and NO3 − concentrati NH4 + concentration over the period from FYs 2003 to 2018. The correlation NH4 + concentration and the {SO4 2− + NO3 − } concentration is very high (r = 0.99 that the NH4 + concentration is determined by SO4 2− and NO3 − concentrations. mean NH4 + concentration decreased from 2.6 μg/m 3 in FY 2003 to 0.96 μg/m 3 a decrease of 1.6 μg/m 3 (63%) over 16 years. The decrease in NH4 + constituted 13.6 μg/m 3 PM2.5 concentration decrease over the 16-year study period. NH4 + probably incidentally reduced by the reductions in SO2 and NOX emissions.

Conclusions
We evaluated the extent to which various countermeasures were effecti ing PM2.5 concentrations in Japan, by analyzing the long-term trends in the con

Conclusions
We evaluated the extent to which various countermeasures were effective in reducing PM 2.5 concentrations in Japan, by analyzing the long-term trends in the concentrations of the major components of PM 2.5 and their corresponding emissions in Nagoya City. PM 2.5 concentrations decreased by 53% (13.6 µg/m 3 ) over the 16-year period from FYs 2003 to 2018 in Nagoya City. Among the components of PM 2.5 , EC showed the greatest decrease (4.3 µg/m 3 ) over the 16-year period, followed by SO 4 2− (3.0 µg/m 3 ), OC (2.0 µg/m 3 ), NH 4 + (1.6 µg/m 3 ), and NO 3 − (1.3 µg/m 3 ). The decrease in EC concentration was found to be due largely to the effect of diesel emission control. Patterns in SO 4 2− concentration indicated that domestic measures had a limited effect and that the effects of transboundary pollution from China, volcanoes, and meteorological factors were significant. OC concentrations were affected by domestic measures, such as VOC emission regulations for stationary sources and the reduction of VOCs from vehicles, construction equipment, etc. NO 3 − concentrations decreased; vehicles contributed most strongly to this decrease, followed by construction machinery etc., and finally stationary sources. NH 4 + concentrations decreased incidentally because of reductions in SO 2 and NO X emissions. In sum, the relationship between the concentrations of the major PM 2.5 components and their corresponding emissions revealed which source measures in Japan were effective in reducing PM 2.5 concentrations. These data relationships also revealed the ineffectiveness of some recent countermeasures for some components, such as those targeting OC concentrations.