Correlation between Atmospheric Boundary Layer Height and

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Correlation between Atmospheric Boundary Layer Height and Polybrominated Diphenyl Ether Concentrations in Air Nguyen Thanh Dien, Yasuhiro Hirai, and Shin-Ichi Sakai Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b03004 • Publication Date (Web): 30 Nov 2016 Downloaded from http://pubs.acs.org on November 30, 2016

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Environmental Science & Technology

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Correlation between Atmospheric Boundary Layer Height and Polybrominated

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Diphenyl Ether Concentrations in Air

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Nguyen Thanh Diena, Yasuhiro Hiraia,*, Shin-ichi Sakaia

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Environment Preservation Research Center, Kyoto University, Sakyo-ku, Kyoto 606−8501, Japan

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Corresponding Author: Yasuhiro Hirai

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Environment Preservation Research Center, Kyoto University, Sakyo-ku, Kyoto 606−8501,

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Japan

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TEL +81-75-753-7712, FAX +81-75-753-7710

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e-mail [email protected]

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Abstract

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In this study, we aim to determine the correlation between the height of the atmospheric

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boundary layer (ABL) and the concentrations of polybrominated diphenyl ether (PBDE)

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congeners, in an effort to improve comprehension of the atmospheric behavior of PBDEs. We

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used the PBDE data in air (n=298), which were measured by the Japan Ministry of 1

ACS Paragon Plus Environment


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Environment (JMOE) at 50 sites across Japan during the period 2009−2012. The height of the

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ABL, which directly affects the PBDE concentrations in the near-surface air, was estimated

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by employing data retrieved from the Japanese global reanalysis (JRA-55) database, using the

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parcel and Richardson number method. The ABL has shown a strong inverse relationship

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with BDE-47 and BDE-99 (p , corresponding to an

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unstable vertical profile, has to be fulfilled. The altitude range from start to end of this lift

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is considered statically unstable.32

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() = () ∙ ( / ()).

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where T(z) is the temperature at height z (K), P(z) is the pressure at height z (Pa), P0 is the

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reference pressure (100000 Pa), and z is the geopotential height above ground (m).

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The Rib is a dimensionless parameter, combining the potential temperature and the vertical

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wind shear. The Rib is used to identify regions of dynamically unstable air.32 The air

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compartment has variable heights to make provision for the formation of the convective

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mixed layer during the daytime and the nocturnal boundary layer and residual layer at night.32

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During the day, as the ground heats up, the air rises and drives convective mixing. At night,

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the air layers near the surface cool much faster, leading to a temperature inversion, which

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prohibits convective mixing. We considered a value of 0.22 for the critical Richardson

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number under unstable condition (convective mixed layer in daytime), and a value of 0.33

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under stable condition (nocturnal boundary layer at night).25,33,34 The Richardson number

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(Rib) is defined by

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= ∙ ()

()

()

Eq 1

∙

Eq 2 9



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where g is the acceleration because of gravity (9.81 m·s-2), !"# is the virtual temperature

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near the surface, !"() is the virtual potential temperature at height z, and v(z) and u(z) are

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the horizontal components of the wind velocity at z. Therefore, the virtual potential

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temperature, mixing ratio, and virtual temperature near the surface were calculated as shown

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in Eqs 3−5

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!" () = () ∙ .∙%

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'() = (#()/(1 − (#())

Eq 4

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!"# = (2,) ∙ (1 + 0.61 ∙ (#(2,))

Eq 5

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where w(z) is the mixing ratio, Hs(z) is the specific humidity, Hs(2m) is the specific humidity

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at 2 m, and T(2m) is the temperature at 2 m (K).

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Subsequently, the ABLs calculated by Rib and PM were subtracted from the geopotential

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height of the surface to estimate properly the effective height of the mixing air that dilutes the

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air pollutants. Since atmospheric PBDEs were continuously monitored for three or seven days

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per sample, the relevant ABL values were estimated as the average of the daytime and

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nighttime for a sampling period, and were subsequently used in the regression analysis. The

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limitation of our study is that the ABL was calculated only at four specific times: 9 AM, 3 PM,

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9 PM, and 3 AM. Although this could cause the biases, estimating an ABL value is preferred

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for an air model rather than assuming a fixed ABL value. Therefore, we think that our

$() .

$()&

∙ ( / ()).

Eq 3

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estimation method is still usable for analyzing the ABL.

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2.3. Tobit regression model for atmospheric PBDE concentrations with censored data

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In environmental science, we take care of the left-censoring (censoring from below), since the

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trace substances in the air, soil, and water are usually presenting the concentrations that are

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lower than limit of detection (LOD). In Japan, the JMOE data showed the variations of LODs

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with time for PBDE congeners (Table S2). The Tobit model, a censored regression model, is

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designed to estimate linear relationships between variables when there is censoring in the

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dependent variable.35 In principle, values that fall at or below some threshold are censored.

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The Tobit model uses the censored data (non-detection) and the uncensored data (detection) in

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a regression procedure. In this model, if the true concentration C* is larger than the LOD

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value, then the observed concentration C is equal to C*. Otherwise, if the true concentration

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C* is less than the LOD value, then the observed concentration C is censored to LOD as

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shown below

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if C* > LOD, C = C*

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if C* ?)3,5, + 7@ ln( A + 1)3 +

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7B C5 + D3 + E3,5,

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where C is the concentration of PBDE congeners (pg m-3); Toutdoor is the outdoor temperature

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(K); Ra is the rainfall rate (mm∙h-1); PD is the population density (person∙km-2); ABL is the

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atmospheric boundary layer height (m); Y is the year (2009−2012); D is a random effect of

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50 sampling sites across Japan; E is an error term that follows normal distribution with mean

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0 and variance F ; a is an intercept; b1,b2,b3,b4, and b5 are regression coefficients; i is the

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sampling location; y is the year; and s is the season.

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The Tobit model was run by the function of panel analysis on the censored model, with the

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left censoring limit using the Stata 13 software (StataCorp LP, Texas, USA).

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In order to clarify the relationship between the ABL and rainfall, we filtered the data into zero

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rainfall (sunny day) and with rainfall (rainy day). Subsequently, we conducted the Tobit

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regression analysis (so-called filtered model) on a rainy dataset by Eq 6 and on a sunny

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dataset by Eq 6 without the term ln(Ra+1).

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In another analysis, we performed a quantitative test to determine the correlation strength

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between the ABL and PBDE congeners by using a dummy variable (so-called dummy model)

Eq 6

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for sunny/rainy day (with 0 and 1), as seen in Eq 7:

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ln 23,5, = 6 + 7 /89:88; 3,5, + 7 ln( A + 1)3 + 7< ln(=>?)3,5, ∗ (1 − HI,,J6K) +

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7@ ln(=>?)3,5, ∗ HI,,J6K + 7B C5 + D3 + E3,5,

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where dummyRain with rainy day=1 and sunny day=0; b3 is a coefficient of ABL in the sunny

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day and b4 is a coefficient of ABL in the rainy day.

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3. Results and discussion

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3.1. Site-specific and time-dependent ABL relevant to PBDE sampling

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As the sampling time for the ABL data (corresponding to the PBDE sampling time) was only

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during early autumn and winter, at specific intervals (three or seven days per sample), the

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estimated ABLs would probably not show all the relevant information on the day/night,

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seasonal, and interannual timescales. Consequently, at first, over a period of four years, we

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analyzed the monthly ABLs for various representative sampling sites on land and sea to

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determine the trends and to confirm our estimation methods of ABL by comparison with the

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previous studies.

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The data relevant to day/night of the urban air (Tokyo metropolitan and Osaka city) showed a

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distinct ABL pattern, namely, this value was much higher during the daytime, as presented in

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Figure 1 (a) and (b). On the other hand, no distinct pattern was observed in the marine ABL

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(the sea around the Chichijima and Okinawa islands), as presented in Figure 1 (c) and (d). The

Eq 7

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contrast between the land (urban) and the sea is attributable to there being a large daily

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exchange of heat and mass between the ABL and the free atmosphere over the land; while,

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over the sea, mixing occurs primarily by turbulent entrainment.36 As shown in Figure 1, the

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seasonal changes in the nighttime ABL (nocturnal boundary layer) are weaker than are those

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of the daytime ABL (convective mixed layer), particularly for urban sites. We therefore paid

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attention to the convective daytime ABL for discussing the seasonal pattern. As a result, the

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ABL height shows the differing seasonal trends for the land and marine sites. As regards the

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urban sites in Tokyo and Osaka, the monthly mean of the ABLs during the warm season

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(spring and late summer) was higher than was that during the cold season (winter) (Figure 1

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(a) and (b) in main text, Figure S1 (a) and (b) in Supporting Information). In contrast, the

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ABLs for the marine sites (Chichijima and Okinawa) were estimated as higher during the cold

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season (winter) in comparison with the warm season (summer and autumn) (Figure 1 (c) and

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(d), Figure S1 (c) and (d)). Furthermore, Figure S1 indicates a consistent seasonal pattern for

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ABLs during 2009−2012 for both land and sea sites.

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In a previous study, using a micro pulse lidar, Chen et al. (2001)37 have measured two ABL

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peaks at the urban area of Tsukuba that occurred in the early spring and autumn during the

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period 1999−2000. In addition, the seasonal pattern at a suburban site near Paris and at urban

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Hong Kong indicated that the ABL was higher during the warm period (autumn, spring, and 14


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summer) compared with the cold period (winter).26,28 Pal and Haeffelin (2015)26 have

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measured diurnal (hourly) and seasonal (monthly) patterns at a suburban site near Paris

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(Palaiseau: 2.208°E and 48.713°N, 160 m above sea level) using lidar equipment. They found

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that the average of the daily maximum values of ABL (ABL_max) was highest during the

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summer (maximum in July) and lowest during the winter (minimum in January). We

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conducted a comparison between the one-year ABL measurement (year 2009) from Pal and

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Haeffelin (2015)26 and the ABL estimation from the JRA-55 reanalysis data for Palaiseau.

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The results pertaining to the highest ABL in summer and the lowest ABL in winter presented

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good agreement between the measurements of Pal and Haeffelin26 and our estimation, as seen

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in Figure S2. The slightly lower ABL_max values from our estimation compared with the

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measurements of Pal and Haeffelin were attributed to the limited number (four) of data points

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per day in the JRA-55 data (1 AM, 7 AM, 1 PM, and 7 PM; France time zone equal to

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UTC+1). On the other hand, Kuribayashi et al (2011)27 observed that the ABL was high in

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winter and low in summer over Okinawa Island and the East China Sea (marine sites), using

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continuously measured radiosonde and lidar data from March 2008 to February 2010. This

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result was consistent with our ABL estimation in Chichijima and Okinawa (marine sites),

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using JRA-55 data. Therefore, these agreements with previously observed ABL data

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enhanced the credibility of our ABL estimates, using reanalysis data. Subsequently, we 15



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present the site-specific and time-dependent ABL relevant to PBDE sampling.

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The ABL distribution plot for all the land (38) and sea (12) sites is presented in Figure 2 (a).

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Figure 2 (a) indicates that the 25% and 75% percentiles of ABL for the warm period were at

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537 and 846 m (median 681 m), whereas, for the cold period, they were at 400 and 1013 m

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(median 667 m). The global planetary boundary layer was estimated by von Engeln and

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Teixeira (2013)30 by using the reanalysis data of the European Centre for Medium Range

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Weather Forecasts (ECMWF). This research30 has shown that over Japan, between the 27th

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and 42nd parallels north, the boundary layer height ranged between 700 and 1400 m. This

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result is in agreement with our estimated ABL, using the JRA-55 database. Figure 2(a)

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indicates that the ABL values during the warm season were only slightly higher than were

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those during the cold season. The reason for the seasonal variation being smaller is the

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differing ABL patterns on land and sea, as discussed earlier. Accordingly, as indicated in

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Figure 2 (b), clearly higher ABL values were found at 38 sites in urban and rural areas (land)

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during the warm season in comparison with the cold season. On the other hand, different

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results were produced for 12 sites at a nearby small island (sea), as shown in Figure 2 (c). The

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day and night patterns of the ABLs for land and sea, land (only), and sea (only) are presented

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in Figure S3 (a) and (b). In addition, the detailed estimation of site-specific and

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time-dependent ABLs for the 50 sites is shown in Table S3. In the next section, we present an

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examination of the correlation between the ABL and temperature.

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3.2. Correlation between the ABL and temperature

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We investigated the relationship between the ABL and temperature by separating three

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datasets, including land and sea sites, land sites, and sea sites because of the different ABL

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patterns on land and sea (as discussed in section 3.1). The histograms of the ABLs for the

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three categorized sites were plotted, as shown in Figure S4 (a), (b), and (c) in the Supporting

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Information. For land and sea sites, there was a weakly positive correlation (n=298,

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coefficient correlation=133, p=0.711, R2=0.0005) between the ABL (lnABL) and temperature

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(1/T) (Figure S5 (a)). In a similar analysis, an extremely weakly positive correlation (n=218,

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coefficient correlation=25.28, p=0.954, R2=0.00002) was found for land sites (Figure S5 (b)).

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On the other hand, for sea sites, there was a significantly positive correlation (n=80,

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coefficient correlation=1665, p

Correlation between Atmospheric Boundary Layer Height and

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