Meteorological and chemical impacts on ozone formation: A case study in Hangzhou, China

Accepted Manuscript Meteorological and chemical impacts on ozone formation: A case study in Hangzhou, China

Kangwei Li, Linghong Chen, Fang Ying, Stephen J. White, Carey Jang, Xuecheng Wu, Xiang Gao, Shengmao Hong, Jiandong Shen, Merched Azzi, Kefa Cen PII: DOI: Reference:

S0169-8095(17)30182-5 doi: 10.1016/j.atmosres.2017.06.003 ATMOS 3967

To appear in:

Atmospheric Research

Received date: Revised date: Accepted date:

14 February 2017 16 May 2017 2 June 2017

Please cite this article as: Kangwei Li, Linghong Chen, Fang Ying, Stephen J. White, Carey Jang, Xuecheng Wu, Xiang Gao, Shengmao Hong, Jiandong Shen, Merched Azzi, Kefa Cen , Meteorological and chemical impacts on ozone formation: A case study in Hangzhou, China, Atmospheric Research (2017), doi: 10.1016/j.atmosres.2017.06.003

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Meteorological and Chemical Impacts on Ozone Formation: A Case Study in Hangzhou, China Kangwei Lia,c, Linghong Chena*, Fang Yingb, Stephen J. Whitec, Carey Jangd, Xuecheng Wua, Xiang Gaoa, Shengmao Hongb, Jiandong Shenb, Merched Azzic, Kefa Cena State Key Laboratory of Clean Energy Utilization, Zhejiang University, Hangzhou 310027, China

b

Hangzhou Environmental Monitoring Center Station, Hangzhou 310007, China

d

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CSIRO Energy, PO Box 52, North Ryde, NSW 1670, Australia

Office of Air Quality Planning & Standards, US Environmental Protection Agency, Research Triangle Park,

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c

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a

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NC 27711, USA

Abstract

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Regional ozone pollution has become one of the most challenging problems in China, especially in the more economically developed and densely populated regions like Hangzhou. In this study, measurements of O3, CO, NOx and non-methane hydrocarbons (NMHCs), together with meteorological data, were obtained for the

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period July 1, 2013 –August 15, 2013 at three sites in Hangzhou. These sites included an urban site (Zhaohui

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“ZH”), a suburban site (Xiasha “XS”) and a rural site (Qiandaohu “QDH”). During the observation period, both ZH and XS had a higher ozone level than QDH, with exceeding rates of 41.3% and 47.8%, respectively. Elevated O3 levels in QDH were found at night, which could be explained by less prominent NO titration effect

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in rural area. Detailed statistical analysis of meteorological and chemical impacts on ozone formation was carried out for ZH, and higher ozone concentration was observed when the wind direction was from the east.

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This is possibly due to emissions of VOCs from XS, a typical chemical industrial park located in 30 km upwind area of ZH. A comprehensive comparison between three ozone episode periods and one non-episode period were made in ZH. It was concluded that elevated concentrations of precursors and temperatures, low relative humidity and wind speed and easterly-dominated wind direction contribute to urban ozone episodes in Hangzhou. VOCs reactivity analysis indicated that reactive alkenes like isoprene and isobutene contributed most to ozone formation. Three methods were applied to evaluate O3-VOCs-NOx sensitivity in ZH: VOCs/NOx

*

Corresponding author. Tel: +86-571-87952647; Fax: +86-571-87951616 E-mail address: [email protected]

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ratio method, Smog Production Model (SPM) and Relative Incremental Reactivity (RIR). The results show that summer ozone in urban Hangzhou mostly presents VOCs-limited and transition region alternately. Our study implies that the increasing automobiles and VOCs emissions from upwind area could result in ozone pollution in urban Hangzhou, and synergistic reduction of VOCs and NOx will be more effective. Keywords: Urban ozone; Meteorological effect; SPM; O3-VOCs-NOx; RIR.

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1. Introduction

The increase in tropospheric ozone is of particular concern in many cities of the world due to its adverse

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impacts on human health and the environment (NRC, 1991). Ozone is a secondary pollutant, formed through

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photochemical reactions involving naturally or anthropogenically emitted NOx, CO, and VOCs (Seinfeld and Pandis, 1998). Due to the complex chemical mechanisms and regional differences of emission distribution and

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meteorology, the relationship between ozone and its precursors (NOx, CO, VOCs) involves highly non-linear interactions (Ahamad et al., 2014; Assareh et al., 2016; Fu et al., 2012; Lin et al., 2015).

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With the acceleration of economic growth and urbanisation in China over the past decades, there has been an enhanced demand for energy and a greater use of fossil fuels, leading to increased emissions of pollutants into the atmosphere (Li et al., 2017; Liu and Wang, 2014; Liu et al., 2012). Regional ozone pollution is

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becoming severe, for example in the North China Plain (NCP), Yangtze River Delta (YRD) and Pearl River

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Delta (PRD), which are three of the most populated and industrialised city cluster areas in China. All of these areas currently suffer from severe air quality degradation (Li et al., 2014; Ran et al., 2011; Tang et al., 2012; Li

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et al., 2012). The studies in these districts indicated that ozone pollution had distinct regional characteristics (Geng et al., 2008; Li et al., 2011; Shao et al., 2006; Tong et al., 2017; Xu et al., 2011; Zhang et al., 2008).

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Hangzhou is located in the eastern part of China, with a population over 8.8 million and an area of 16,596 sq. kilometres. It is the second largest city in the Yangtze River Delta (YRD), and has undergone rapid recent economic growth. For example, the number of automobiles has increased from 0.39 million (2000) to 2.52 million (2013). Bao et al. (2010) studied the characterisation and source apportionment of PM2.5 and PM10 in Hangzhou. Hong et al. (2009) measured C2-C12 hydrocarbons over a short period of time in five typical areas of Hangzhou. Ying et al. (2012) analyzed VOC reactivity in the ambient air around urban traffic roads in Hangzhou. These studies showed that the emissions of NOx, VOCs and CO have increased significantly in recent years. The concentrations of ozone measured at the observation sites are very high in the summer.

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However, the characteristics of ozone formation and its relationship to NOx and VOCs is still unclear in Hangzhou. In addition, the meteorological and chemical impacts on ozone episodes are still unclear. In this paper, an approach incorporating time series analysis, meteorological data, correlation analysis, VOCs reactivity analysis and O3-VOCs-NOx sensitivity analysis was developed. Time variations of O3, CO, NOx, and VOCs were presented, followed by a detailed analysis of meteorological and chemical impacts on

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ozone formation in the urban area (ZH). The differences between ozone episode and non-episode days are discussed. The sensitivity of ozone production to its precursors was evaluated using VOCs/NOx ratio method

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(Zou et al., 2015), Smog Production Model (Blanchard et al., 1999; Blanchard and Stoeckenius, 2001) and

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Relative Incremental Reactivity (Lu et al., 2010). The results can be used to evaluate key factors of atmospheric

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ozone formation and identify strategies for controlling photochemical pollution in Hangzhou.

2. Material and methods

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2.1 Measurements and instruments

Measurements of O3, VOCs, NOx and CO were taken during the period between July 1, 2013 and August 15, 2013 at three stations in the Hangzhou region. Fig. 1 shows the location of the observation sites selected for

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this study. The Zhaohui (ZH) site is the most equipped station, with on-line VOC measurement for 56 species.

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The location is on the top of a residential building (20 m in height). This area is densely population at 30,000 people per sq. kilometre. The satellite image showing the location of the ZH site is shown in Fig.S1. It is clear

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that the ZH site is in a high traffic district, surrounded by many major roads and highways. Except for traffic and the residential emissions, no major pollution sources are found in the area within a 3 km radius. The Xiasha

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(XS) site located in a suburban district, situated in an economic-technological development zone (30 km east of ZH). This area is full of pollution-intensive industries, such as petroleum refineries, paint manufacturing and food product factories. The Qiandaohu (QDH) site is located 170 km southwest of ZH, considerable distance from the urban centre of Hangzhou. The site is located in a scenic spot, and there are no major anthropogenic emission sources in the surrounding areas. The observations at the three sites of NO2 and CO (Fig. 4), as well as SO2, PM2.5, PM10 (Fig. S2), show that QHD has near-zero emission compared to other two sites. Therefore, QDH is considered a background site. The measurement parameters and site location information are shown in Table 1. Commercial on-line trace

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gas monitoring instruments were used to continuously monitor various trace gases. Surface ozone was measured with a UV photometric analyzer (Thermo Scientific, Model 49i) with a detection limit of 0.5 parts per billion (ppb) and a precision of 1 ppb. CO was measured with a gas filter correlation analyzer (Thermo Scientific, Model 48i). A NOx analyzer (Thermo Scientific, Model 42i) was used to measure the NOx (NO+NO2) in ZH, using a chemiluminescence method, while Differential Optical Absorption Spectroscopy (DOAS, Opsis

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AB, Model AR500S) was used to measure NO2 at XS and QDH. In addition, ambient VOCs were measured in ZH by using an on-line GC-FID/PID (Syntech Spectras GC 955-611/811), and the data were recorded at a high

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time resolution (every 30 min). A mixture of 56 NMHCs (Spectra Gases, Inc., Newark, New Jersey, USA) was

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used as a calibration standard for the on-line GC-FID/PID system. O3, NO, NO2 and CO were monitored with a time resolution of 5 min, and average concentrations at intervals of 1h were used in this study. The QA/QC

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routines, including the daily zero/standard calibration, span and range check and station environmental control, were based on the guidelines established by the USEPA (1998).

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meteorological

data

for

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2.2 Meteorological condition Hangzhou

city

was

obtained

from

Weather

Underground

(http://www.wunderground.com). The general weather conditions during the observation period are shown in

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Fig. 2. The mean temperature and relative humidity (RH) were 32.5°C and 55.1%, respectively. Both presented

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a typical uni-modal diurnal pattern. The atmospheric pressure did not change significantly, remaining steady around 1000 hPa. Although 10 days of precipitation occurred during the observation period, the total

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precipitation was only 49 mm. Fig. 3 shows the wind rose for the entire observation period. The frequency of wind direction (WD) was divided into four wind speed (WS) ranges: ≤2 m/s, 3–4 m/s, 5–6 m/s and ≥ 7 m/s.

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During the observation period, the wind field was dominated by WSW, SSW, S and E wind with a wind frequency of 16%, 16.3%, 14.2%, and 12.6%, respectively. Generally, the wind speed was below 4m/s, and stronger winds mostly came from WSW and E. 2.3 Smog Production Algorithm The Smog Production Model (SPM) is an observation-based model (OBM) using a series of semiempirical formulae, which is used to calculate the degree or extent of photochemical reaction. It is widely used to determine whether VOCs or NOx limit further ozone formation (Li et al., 2014; Peng et al., 2011; Sun et al., 2010; Tang et al., 2012). Johnson (1984) first proposed the idea of "Smog Production", with Blanchard et al.

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(1999) reformulating the SP algorithm using smog chamber data and model simulation results, improving the accuracy of forecasting ozone sensitivity. The extent of reaction E(t) was defined as SP(𝑡)

E(𝑡) = SP

max

=

O3 (𝑡)+DO3 (𝑡)−O3 (0)+NO(𝑖)−NO(𝑡) 𝛽[NO𝑥 (𝑖)]𝛼

(1)

In Eq.(1), all species are expressed in units of volume mixing ratios. SP represents the total amount of NO

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consumed and O3 produced (Chang and Suzio, 1995). DO3(t) represents the accumulated deposition losses of

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ozone at time t, which can be estimated by O3(t), dry deposition velocity Vd(t) and planetary boundary layer height z(t), according to Blanchard et al. (1999). O3(0) is the background concentration of ambient ozone, with

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a value of 40 ppb used in this study. NOx(i) represents the concentration at time t corresponding to the mass of NOx input to the system, from time zero to time t, and there are two ways to calculate based on different

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assumptions. When NOy (the sum of NOx and NOx reaction products) data are available, the NOx(i) is estimated as the sum of NOy and the cumulative mass of NOy lost to deposition in Eq. (2), and the

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corresponding reaction extent was denoted as E(t)_NOy.

NOx (𝑖) = NOy(𝑡) + DNOy(𝑡)

(2)

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When true NOx (NO + NO2) data are available, the NOx(i) is estimated from Eq. (3)–(6), and the corresponding

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reaction extent was denoted as E(t)_NOx (Blanchard et al. 1999). 𝛽

NOx (𝑖) = NOx (𝑡) + [ ] [2𝑋 + 1]3

Where 3 27(𝛾/F)

]

(4)

(5)

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𝐶 = 1 − [2(𝛽/F)]3

(3)

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4𝜋+cos−1 (𝐶)

𝑋 = cos [

3F

γ = O3 (𝑡) + DO3 (𝑡)−O3(0) + F ∗ NOx(𝑡) − NO(𝑡)

(6)

We used observational data for O3(t), NO(t) and NOx(t), and fixed values of 2/3 for α, 19 for β and 0.95 for F, consistent with the procedure outlined by Blanchard et al. (1999). The explanations for the SPM results are as follows: the reaction extent E(t)＜0.6 represents a system under VOCs control, E(t)＞0.9 represents a system under NOx control, and 0.6＜E(t)＜0.9 represents the transition control region (Blanchard and Stoeckenius, 2001). It should be noted that the applicability of these parameters to different cities is always a challenging

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problem. In the absence of improved SPM method or a better solution for the current fixed parameters, we have to use them in their current state as they maintain compatibility and comparability to other field studies using the same SPM method. Since the measured NOx (from thermo model 42i instruments) is neither true NOx (NO + NO2) nor true NOy (NOx + PAN + HNO3 + other oxidized nitrogen species), as suggested by Blanchard and Fairley (2001), the mean of two calculations is used here as our best estimate of the true extent of reaction.

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Fig.S4 also confirmed that the biased measurements underestimate extent when used as NOx and overestimate

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extent when used as NOy.

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2.4 Relative Incremental Reactivity (RIR)

Relative Incremental Reactivity (RIR) is a parameter which is widely used to evaluate the controlling

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factors for photochemical ozone production and O3-VOCs-NOx sensitivity in many regions (Wang et al., 2017; Zhang et al., 2007; Zhang et al., 2008). It was developed by Cardelino and Chameides (2000), using a

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photochemical box model containing the CB-IV mechanism. Two-phase simulations are performed during the calculation process (Lu et al., 2010). The first phase assimilates the values of input species to calculate secondary products and determine a pseudo-emission term S(X). S(X) represents the combined flux of local

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emissions and regional transport which characterizes the local impact of regional emissions. In the second

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phase, S(X) is varied to derive RIR(X) for the major O3 precursors using the following equation:

 PO  NO (X)  PO3  NO (X  ΔX)  /PO3  NO (X) RIR(X)   3 ΔS (X)/S (X)

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(7)

Here, PO3-NO is the daytime integrated O3-NO weighted by the mixed layer height, which is used as the

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ozone formation potential. RIR(X) represents the ratio of photochemical O3 production (percentage change, %) to the source effect (percentage change, %). X represents the primary air pollutants, such as NO, CO, AVOC (anthropogenic VOC, including the CB-IV mechanism species PAR(alkane), OLE(active olefins), ETH(ethene), TOL(inert aromatics) and XYL(active aromatics)) and BVOC (biogenic VOC, including the CB- IV mechanism species ISO(isoprene)). Measurements of input parameters such as O3, NO, CO, VOCs, temperature and pressure were used as model constraints at 1 h intervals from 7:00 to 19:00. Initial values of the unconstrained trace gas species were set to zero. 3. Results and discussion

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3.1 Air pollution characteristics in urban, suburban and background sites Fig. 4 shows the time series of the hourly average data for O3, NO, NO2, CO and VOCs at the observation sites during the period from July 1, 2013–August 15, 2013. The concentrations of all species presented as “sawtoothed” patterns, indicating that the pollutant concentrations gradually accumulated to high levels and before decreasing to low levels. This may was likely due to the diurnal variation of the planetary boundary layer

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(PBL), the cycle of anthropogenic emissions and photochemical processes. In general, the concentrations of O3, NO2 and CO at both ZH and XS sites were close, but higher than those at QDH. The statistical summary of the

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daily average concentrations, standard deviations and maximum value of O3 and its precursors at each site are

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given in Table 2. The mean concentration of O3 in QDH was 82.3±21.1 µg/m3, 83.9±26.5 µg/m3 in XS and 89.1±29.6 µg/m3 in ZH.

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TVOC (Total Volatile Organic Compounds) concentrations in ZH ranged from 12.8 ppb to 165.6 ppb, with an average of about 55.9 ppb. The total average VOCs were comprised of: alkanes 33.2%, alkenes 25.9%,

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aromatics 24.3%, and acetylene 16.6%. The composition of VOCs is similar to those in urban areas of Beijing and Shanghai (Chen et al., 2012; Shao et al., 2009; Wang et al., 2010; Xu et al., 2011). An ozone exceedance day is defined as one where the daily maximum O3 concentration exceeded 200

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µg/m3 (according to Chinese Ambient Air Quality Grade ⅡStandard GB3095-2012). This is represented in Fig.

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4(a) by the red line. Taking ZH as an example, three O3 exceedance episode periods (10–12 July, 24–27 July and 7–12 August) were found coinciding with high concentrations of NOx, CO, and VOCs. During the

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observation period, the proportion of days in which ozone was above the standard in ZH and XS was 41.3% and 47.8% respectively. In comparison, ozone was only exceeded two days at QDH, as a background site in a

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more rural area.

The diurnal variations of O3, Ox(O3+NO2), CO, NO2, NO, NOx and VOCs are shown on Fig. 5. In general, O3 diurnal patterns in ZH and XS were similar, reaching a maximum at 13:00–14:00 and a minimum in the early morning at 5:00. As expected, the O3 concentration rose rapidly once the photochemical processes began. The O3 level in QDH was much lower than that in ZH and XS during daylight hours, while a higher level of O3 in QDH was found during nighttime hours. The relatively higher level of nighttime O3 at QDH is explained by the weaker NO titration effect at night. In contrast, the urban area has much higher NOx emissions, and the strong NO titration effect leads to lower nighttime O3 in these urban areas (ZH/XS) compared to background

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site (QDH). Fig. 5(c) and (d) show that O3 precursors in QDH, like CO and NO2, much lower than those in ZH and XS, which can explain the lower O3 production rate in QDH during the daytime. Both CO and NO2 variations show lower concentrations in the daytime and higher concentrations at night, which may be affected by the local emission characteristics, UV radiation and the diurnal cycle of PBL. Similar results have been observed in other cities in previous studies (Geng et al., 2008; Xu et al., 2011).

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The “potential ozone” factor Ox(O3+NO2) was used as an estimate of the total atmospheric oxidant potential (Jenkin, 2004; Lin et al., 2008). In Fig. 5(b), the variations of Ox show similar patterns compared with

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O3, and the peak value lasts well into the afternoon. The Ox level in ZH and XS were similar, but both higher

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than that in QDH, which show that the former sites had much higher atmosphere oxidizing ability. The variations of NOx and VOCs in ZH demonstrated opposite patterns compared with O3, together with

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the valley values at 13:00–14:00. The variation of NO at this site was nearly a uni-modal pattern, with a morning peak at 6:00–7:00, corresponding to morning rush hours. Then, NO decreased sharply due to the

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photochemical reaction that occurs with increasing O3.

3.2 Meteorological and chemical impacts on ozone formation in the urban area 3.2.1 Temperature and relative humidity

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Temperature is usually used as a predictor of O3 episode events (Steiner et al., 2010; Wise, 2009) because

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of its direct impact on chemical kinetic rates and the mechanism pathway for the formation of O3 (e.g., Habstraction versus OH addition (Atkinson, 1990)). As shown in Fig. 6(a) and (b), the ground level ozone

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concentrations present a direct positive correlation with temperature, and the ozone-temperature slope in the daytime is more significant than it is at night. However, it should be noted that the observed correlation

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between the O3 mean concentration and the temperature cannot just be attributed to temperature dependence of chemical reaction rates alone, because high temperatures usually have strong correlations with stagnant, sunny, or high UV radiation atmospheric conditions. Fig. 6(c) and (d) show the dependence of the O3 mean concentration on RH for day and night. It is clear that the O3 mean concentrations decrease as RH increases regardless time of day. A reasonable explanation would be that high RH in the summer is associated greater chance of rainfall, therefore weak UV radiation might reduce ozone production rates, whereas high temperatures and high levels of UV radiation mostly accompany low RH in the summer, which would be favourable for O3 formation.

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3.2.2 Wind direction Fig. 7 shows the wind frequency roses of O3 concentrations for the daytime (06:00–19:00) and nighttime (20:00–05:00). The frequency of wind direction (WD) was divided into five O3 concentration ranges: 0–49, 50– 99, 100–149, 150–199, and ≥200 µg/m3. During the daytime, the prevailing surface wind directions were SSW, WSW, E, and SW, with a wind frequency of 16.6%, 16.1%, 14.7%, and 14.2%, respectively. At night, the

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prevailing wind direction turned to S, with a wind frequency of 36.4%. The highest ozone concentrations were predominantly observed when the wind was travelling from the easterly direction. Previous studies (Wang et al.,

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2014; Wei et al., 2014) have showed that industry such as petroleum refinery and paint manufacturing are

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major emission sources of VOCs in China. The XS site is located in a chemical industrial park located 30 km east of the ZH site. Therefore, it is highly possible that the XS area is one of the causes of VOC emissions

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leading to strong ozone pollution in the Hangzhou urban area. Conversely, 20 km in a south-west direction from the ZH site is primarily comprised of a mountain range, with fewer potential emission sources. This can

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explain why the ozone concentrations were not as high as that from the E direction. Based on the above analysis, regional ozone characteristics could be detected. O3 concentrations occurring when the wind was from the E direction represented the ozone pollution characteristics of the

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Hangzhou urban area, while winds from the SSW, WSW, and SW were less polluted.

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3.2.3 Correlation analysis of O3 with its precursors and meteorological parameters The data were grouped into subsets representing the whole day (00:00–24:00), daytime (06:00–19:00), and

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nighttime (20:00–05:00). The Pearson’s correlations of O3 with its precursors (NO2, NO, NOx, CO, VOCs) as well as meteorological parameters, were calculated from the hourly mean values of the quantities. Internal

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relations, such as the influences of different precursors on O3 and the homology of different primary pollutants are recognised through the correlation coefficients. As listed in Table 2, O3 is mostly negatively correlated with its precursors (except for being positively correlated with CO at night, where the correlation coefficient is just 0.0058). All of the precursors have a higher magnitude of correlation with O3 during the day than at night, as explained by that the ozone is formed primarily from photochemical processes. NO, NO2, NOx, CO and VOCs were positively and significantly correlated with each other, regardless of whether it was during the day or at night, suggesting that they were dominantly affected by some common factors and were mostly emitted from similar or co-sourced emission

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sources. During the entire day, the absolute correlation coefficient (R) value of the VOCs-NOx correlation was much larger than that of the VOCs-CO correlation, demonstrating that VOCs were more closely linked with NOx than with CO. For the entire day, the temperature (T) was mainly positively correlated with O3 and negatively correlated with the O3 precursors, while relative humidity (RH) was negatively correlated with O3 and positively

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correlated with O3 precursors. The correlation coefficient (R) of the T-RH correlation was as high as -0.9353, thus possibly indirectly causing the opposite relationships above. Wind speed (WS) was not significantly

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correlated with O3 and the absolute R-value of the O3-WS correlations for the daytime and nighttime were -

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0.01843 and 0.16109, respectively. It should be noted that O3 has a typical diurnal variation, maintaining the highest value for the entire day between 11:00–16:00. In order to avoid the influence of O3 diurnal variations on

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the correlations, hourly mean data at 11:00–16:00, instead of the 24 hourly mean data, were used to make a further analysis. The mean concentrations of O3 under different WS are shown in Fig. 8. The O3 mean

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concentration decreased as WS increased, demonstrating the fact that high wind speed promoted atmospheric mixing, dispersion, and transport, hence favouring the dilution of O3. 3.3 Ozone formation in urban areas: episode vs. non-episode

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As shown in Fig. 4(a), three O3 episode periods (10–12 July, 24–27 July, 7–12 August) were observed

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during the study. To exclude the effect of rainy days, a non-episode period without precipitation (14–19 July) was selected for comparison with the episode periods. The average values of trace gases and meteorological

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parameters in these periods are listed in Table 3. The mean concentration of ozone in the non-episode periods was 82.4 µg/m3, while the values in the episode periods were 120.8 µg/m3, 103.4 µg/m3, and 141.1 µg/m3,

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respectively. Possibly due to the weak emission and increased regional transportation effect between July 14– 19, the levels of ozone precursors (NOx, CO, VOCs) during the non-episode period were much less than in the episode periods, which could be one of the important causes for the low ozone level. As discussed, both high T and low WS strongly promote ozone formation in this region. This is shown explicitly in Table 3, where high average temperatures (T) and low wind speeds (WS) were observed during the three episode periods compared with the non-episode period. A high temperature was usually accompanied by high UV radiation atmospheric conditions, increasing the ozone production rate. However, WS does not favour the diffusion of trace gases, causing the accumulation of ozone and its precursors. Therefore, high T and low

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WS meteorological conditions can be the second explanation for ozone episodes. As shown in Fig. 9, ozone concentrations were the highest under the E direction during the three episode periods, especially in episode period Ⅲ. It is clear that S is the dominant wind direction during the non-episode period, with wind frequencies of 37.2%. In contrast, the prevailing surface wind directions of the three O3 episode periods were E, WSW, and E, with wind frequencies of 41.5%, 30.1%, and 20.6%, respectively.

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Although WSW was the dominant wind direction for the episode period Ⅱ, the largest impact on high ozone occurrences may be easterly-blowing winds, with the second highest wind frequency of 19.3%.

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Meanwhile, the average O3 concentration was 103.4 µg/m3, which was lower than were the other two episode

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periods. This may have some association with the prevailing wind from WSW.

We have provided diurnal cycles of gaseous pollutants and meteorological parameters for different periods

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in Fig. S3. Compared to O3 non-episode period, we concluded that a higher concentration of precursors, higher T, lower RH, lower WS and eastern-dominated WD contribute to O3 pollution in urban Hangzhou.

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Propene-Equivalent concentrations (Prop-Equiv) and ozone formation potential (OFP) were used to estimate the ozone formation contribution of each VOCs species. The Prop-Equiv concentration was estimated

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according to their kinetic reactivity (Chameides et al., 1992; Lawrimore et al., 1995). 𝑘OH (𝑗) 𝑘OH (C3 H6 )

(8)

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Prop − Equiv conc(𝑗) = number of carbon × conc(𝑗) ×

where j represents a species of VOC, conc(j) represents concentration of each VOC in ppb. kOH(j) and kOH(C3H6) denote the chemical reaction rate constant of species j and propene with OH respectively.

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OFP represents the maximum ozone concentration generated by this species (Carter, 1994), and is

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determined by its concentration and maximum incremental reactivity (MIR). OFP(𝑗) = conc(𝑗) × MIR(𝑗) (9)

where MIR(j) is the maximum incremental reactivity coefficient for species j. As shown in Fig. 10, fractions of Prop-Equiv and OFP for different VOCs groups consist well. Although the sum of alkene and aromatic contributed ~50% volume mixing ratio, alkene and aromatic contributed 56~60% and 25~31% ozone formation during whole observation, respectively. Furthermore, top 10 VOCs species based on Volume Mixing Ratio (VMR), Prop-Equiv and Ozone Formation Potential (OFP) during whole period were listed in Table 4. The reactive alkene species like isoprene and isobutene contributed most to ozone formation, which is consist with the RIR results in Sec 3.4.3.

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3.4 Ozone photochemical production in urban area: O3-VOCs-NOx 3.4.1 VOCs/NOx ratio analysis Assessing whether an area in which ozone pollution is either a NOx-limited or VOCs-limited regime is an important step in developing effective control strategies. The relationships between ozone and its precursors are crucial for understanding the local ozone photochemical processes. In theory, controlling NOx, VOCs, and other

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precursors can limit ozone formation. However, existing studies of ozone and its precursors shown its formation to be highly non-linear (Seinfeld and Pandis, 1998). The EKMA (Empirical Kinetic Modeling

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Approach) is one approach that has been used to investigate the sensitivity analysis with O3-VOCs-NOx

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(Seinfeld, 1989). Using this approach, if the critical approximate value for the VOCs/NOx ratio is 8 (NRC, 1991). That is, when VOCs/NOx is smaller than 8, ozone formation is under VOCs-limited conditions; whereas

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when VOCs/NOx is higher than 8, it is under NOx-limited conditions. When the ratio is between 4 and 15, reduction in both VOCs and NOx can have a positive impact on O3 control. However, when VOCs/NOx is

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extremely low (＜4:1), it is under strong VOCs-limited and reduction of NOx could lead to an increase O3 instead. This simple and feasible method, based on field observations of ambient ozone precursors, has been widely exploited for investigating this issue (Prabamroong et al., 2012; Strong et al., 2013; Jia et al., 2016).

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If local photochemical ozone formation is considered only, without other factors, such as regional

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transportation, the early morning data for VOCs/NOx ratio is regarded as being the most relevant for an ozone sensitivity analysis (Ran et al., 2009). As shown in Fig. 11(a), early morning VOCs/NOx ratios were almost

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always in the range between 4:1 to 15:1 (average 8:1), indicating that summer O3 formation in urban Hangzhou was partly under both VOCs-limited and NOx-limited. Meanwhile, the blue and red scatters (where O3 daily

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maximum value higher than 200 μg/m3) were nearly in the range of 8:1 to 15:1, suggesting that high episode of O3 formation were more likely under NOx control. Zou et al. (2015) found similar characteristics in O3 pollution period in suburban areas of Guangzhou. On the other hand, noontime and late afternoon VOCs/NOx ratios also could provide additional information for O3-VOCs-NOx sensitivity during different periods of whole day. As shown in Fig. 11(d), the average diurnal variation of VOCs/NOx presents similar pattern with O3 (Fig. 5(a)), reaching the maximum at noontime, which suggests that O3 formation likely turned to NOx-limited condition during daily O3 episode period. 3.4.2 Smog production model

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Some of the limitations in the oversimplified VOCs/NOx ratio approach mentioned above are: (1) the empirical critical value of 8 may not be suitable in Hangzhou; (2) regional transport of pollutants has not been considered; and (3) the VOCs/NOx ratio may be undervalued because only 56 VOC species (most of them are anthropogenic) were included. In addition, the impact of biogenic VOCs on ozone sensitivity analysis has been emphasized in recent research (Strong et al., 2013; Tsimpidi et al., 2012). Consequently, it is complementary to

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conduct another alternative analysis using the Smog Production Model (Li et al., 2014; Peng et al., 2011; Tang et al., 2012). The Smog Production Model (SPM), based on the field observations of ambient O3, NO and NOx

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concentrations, was applied to the analysis of ozone control strategies. The detailed explanations for SPM can

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be found in Sec. 2.3.

As shown in Fig. 12(a), during the 46-day observation period, there were 23 days with reaction extent

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values E(t)＜0.6, 10 days with 0.6＜E(t)＜0.9, and 13 days with E(t)＞0.9. This indicates that urban area in Hangzhou is VOCs-sensitive and transition region alternately, which is slightly different than the results from

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the VOCs/NOx ratio method. 3.4.3 Relative incremental reactivity

Based on previous analyses in Sec. 3.4.1 and Sec. 3.4.2, both the VOCs/NOx ratio analysis and SPM can

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only give qualitative conclusions for sensitivity of ozone formation to VOCs and NOx. However, for the

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development of further control strategies for urban O3 in Hangzhou, in addition to determining whether the airshed is under NOx-limited or VOCs-limited condition, determination of the specific VOCs species that are

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most responsible for ozone formation is required. In this section, another observation-based model was applied to calculate relative incremental reactivity (RIR) for different O3 precursors, which can give a quantitative

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results for ozone key control factors (Zhang et al., 2008). The explanation for RIR results is as follows: when RIR(NO)＜0, O3 is considered under VOCs control, and negative RIR(NO) represents reduction non-benefitial effects (reduction of NOx leads to increase O3); when RIR(VOC) ＜RIR(NO), O3 is considered under NOx control; and when RIR(VOC)＞RIR(NO) ＆ RIR(NO) ＞0, O3 is considered under transition region (Lu et al., 2010). Relative incremental reactivities (RIR) of AVOC (anthropogenic VOC), BVOC (biogenic VOC), CO, and NO for urban Hangzhou (ZH) are shown in Fig. 12(b). There are no results for July 12, 14 and August 3, 4, 12 due to incomplete input data. In general, 20 VOCs-sensitive days, 16 transition region days and 5 NOx-

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sensitive days were identified acoording to RIR results, which is consistent with the analysis of SPM. Therefore, both SPM and RIR indicate that the urban area in Hangzhou alternates between being VOCs-sensitive and in the transition region. On most days, RIR (AVOC) is much higher than RIR (BVOC), which indicates that AVOC is the major factor. Further analysis in Fig. 12(c) shows the most important AVOC species which contributed to O3 formation was OLE (active olefins), which should be targeted for priority control.

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O3 photochemical sensitivity types (ZH) for SPM and RIR during observation period were summarized in Table 5. 15 days were identified as VOCs-limited, with high consistency of 65.2~75.0% between conclusions

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for both SPM and RIR; while 7 days were identified as transition region with 43.8~70.0% consistency.

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However, there were difference between the NOx-limited results for the two OBM methods, with a large consistency range of 30.8~80%. The reason might be attributed to uncertainty of assessment criteria and for

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SPM and RIR, which still needs further study.

Moreover, different O3 episode periods present different O3-VOCs-NOx sensitivity relationships. As shown

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in Fig.12, episode period Ⅰ represents transition region; episode period Ⅱ represents “VOCs-limited ＆ transition region”; and episode period Ⅲ represents “NOx-limited region”. However, in most non-episode periods, O3 was primarily controlled by VOCs. The ozone sensitivity varies on a case by case basis for different

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episode periods, showing the complexity of O3 formation in urban Hangzhou. However, the general conclusion

within the transition region.

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can be drawn for O3 formation in urban Hangzhou is that it is usually under either VOCs-limited condition or

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We summaried O3-VOCs-NOx sensitivity studies using different methods over China (Table 6). It shows that urban areas tend to be VOCs-sensitive, which is sight different from our finding for urban Hangzhou. In

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addition, most areas determined that reactive alkene and aromatics are the most important contributors to ozone formation, which is consistent with VOCs reactivity analysis (alkene and aromatic contributed 56~60% and 25~31%) in this study. Therefore, the more effective approach for ozone reduction in urban Hangzhou may be synergistic reduction of both reactive alkenes & aromatics and NOx rather than reduction of VOCs alone.

4. Conclusions Measurements of ozone and its precursors, CO, NO, NO2, and VOCs, together with meteorological data, were obtained July 1, 2013–August 15, 2013, at three sites in the Hangzhou region. Detailed characteristics of

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ozone formation from its precursors in an urban, a suburban and a rural site were presented. Meteorological and chemical impacts on ozone formation at the urban area were investigated. Observation-based analyses were performed to investigate the characteristics of ozone and its precursors at the three sites. During the observation period, both urban and suburban areas had a higher ozone level than the rural area, with the number days of ozone exceedance being 41.3% and 47.8%, for the urban and suburban sites

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respectively. Compared to other urban/suburban sites, higher O3 levels in rural areas were found during nighttime hours, which is explained by less prominent NO titration effect at the rural site due to lower overall

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NOx concentrations.

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A detailed statistical analysis of the meteorological and chemical impacts on ozone formation was carried out in the urban area, and the highest ozone concentrations were observed when the wind was blowing from an

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easterly direction. Four specific periods, three ozone episode periods and one non-episode period, were selected and compared during the study, with the results demonstrating that the ozone episodes were caused by high

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concentrations of precursors, higher temperatures, lower RH and lower wind speeds. Meanwhile, the high correlation between easterly wind transportation and ozone pollution events in the downwind area was reconfirmed. Prop-Equiv concentration and ozone formation potential (OFP) were applied for VOCs reactivity

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analysis. It was shown that alkenes and aromatics contributed to 56~60% and 25~31% of ozone formation

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during the observation period, respectively.

The O3-VOCs-NOx relationships were further explored using the VOCs/NOx ratio approach, SPM and RIR.

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The results showed that O3 photochemistry is predominantly under either the VOCs-limited condition or transition region condition in urban Hangzhou during the summer. Anthropogenic VOCs, particularly reactive

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alkenes, are the largest contributors to O3 formation, and present an effective target for ozone control measures. Our study implies that the increase of automobiles and pollutant emissions from the upwind area (industrial area) could result in an enhancement of ozone in urban Hangzhou, and synergistic reduction of reactive alkenes, aromatics and NOx will be more effective. However, it is worth noting that the conclusion that ozone formation is primarily under “VOCs-limited and transition region alternately” applies only to Hangzhou urban area during summer, and further investigation is needed for suburban/rural areas in the future.

Acknowledgements

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This work was supported by the Public Project of Ministry of Environmental Protection (201409008-4), the National Basic Research Program of China (2015CB251501), the Project of Hangzhou G20 Environmental Protection (2016-004), and the Program of Introducing Talents of Discipline to University (B08026).

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15(12), 6625-6636.

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Table 1. Location and measurement information for the three different sites Latitude(°N)

Longitude(°E)

Site Classification

Air Pollutants Measured

ZH

30.289722

120.156666

Urban/traffic

CO,NO,NO2,O3,VOCs

XS

30.305833

120.348055

Suburban/industrial areas

CO,NO2,O3

QDH

29.635576

119.029129

Rural/background station

CO,NO2,O3

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CE

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D

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NU

SC

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Station

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Table 2. Pearson’s correlation coefficients among O3, its precursors and meteorological parameters (ZH) O3

1

NO

-0.32414

0.3712

1

NOx

-0.4375

0.95251

0.63633

1

CO

-0.1571

0.49808

0.46242

0.57256

1

VOCs

-0.32085

0.71165

0.43557

0.75459

0.55873

T

0.80146

-0.40099

-0.40347

-0.43276

-0.1896

RH

-0.73005

0.43433

0.47332

0.49282

0.28061

WS

0.21047

-0.39896

-0.30389

-0.46534

-0.37564

O3

1

RH

WS

1

NO

-0.45772

0.46924

1

NOx

-0.40099

0.94526

0.73171

CO

-0.14248

0.46151

0.50362

VOCs

-0.23396

0.67061

0.44371

0.7599

-0.46621

-0.5763

RH

-0.68939

0.46257

WS

-0.01843

-0.2568

O3

1

NO2

-0.10598

NO

-0.35516

NOx

-0.18706

CO

0.0058

1

1

-0.29534

1

0.3655

-0.9353

1

-0.4266

0.28271

-0.3276

1

1

0.70616

0.53071

1

-0.51935

-0.28438

-0.3362

1

0.59904

0.53691

0.34434

0.37441

-0.93393

1

-0.32566

-0.37799

-0.348

-0.32681

0.0783

-0.11339

MA

0.56743

0.33456

1

0.95542

0.59789

1

0.50738

0.41152

0.56388

1

VOCs

-0.15466

0.69414

0.46609

0.77652

0.56039

1

T

0.55507

0.14614

-0.35512

0.00946

0.14962

0.09294

1

RH

-0.44602

0.04732

0.48201

0.21716

0.07668

0.11967

-0.86414

1

0.16109

-0.38364

-0.35712

-0.4683

-0.36018

-0.45342

0.12979

-0.30192

WS

1

SC

-0.37075

NU

NO2

AC

(20:00–05:00)

T

RI

-0.44355

T

Nighttime

VOCs

1

D

(06:00–19:00)

CO

NO2

PT E

Daytime

NOx

1

CE

Entire Day

NO

PT

O3

NO2

*Correlation is significant at the 0.05 level (2-tailed).

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Table 3. Average trace gas concentration and meteorological parameters during three O3 episode periods and one non-episode period (ZH) TVOC

T

RH

WS

(ppb)

(ppb)

(°C)

(%)

(m/s)

120.8

5.6

57.1

63.8

933.5

26.0

19.1

18.3

11.8

75.2

32.7

53.6

2.4

103.4

6.3

44.3

56.4

773.8

19.0

10.6

19.1

15.4

64.1

34.1

52.2

2.2

141.1

4.6

38.5

49.3

909.2

21.7

15.4

17.4

13.7

68.1

34.5

47.1

2.4

82.4

2.6

28.2

30.7

438.8

11.8

11.8

6.1

5.1

34.8

31.8

52.3

3.5

PT

Acetylene

(ppb)

RI

(July 14–19)

Aromatics

(ppb)

SC

O3 non-episode period

Alkenes

(ppb)

NU

(August 7–12)

Alkanes

MA

O3 episode period Ⅲ

CO (µg/m3)

D

(July 24–27)

NOx (µg/m3)

PT E

O3 episode period Ⅱ

NO2 (µg/m3)

CE

(July 10–12)

NO (µg/m3)

AC

O3 episode period Ⅰ

O3 (µg/m3)

24

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Table. 4. Top 10 VOCs species based on Volume Mixing Ratio (VMR), Prop-Equiv and Ozone Formation Potential (OFP) during whole period in ZH site. species

VMR (ppbv)

%

species

Prop-E (ppbC)

%

species

OFP (ppbv)

%

1 2

acetylene isobutene

9.30 5.79

16.63 10.35

isoprene isobutene

47.37 45.24

22.41 21.40

isobutene isoprene

36.40 22.45

18.99 11.71

3 4

2,3,4-trimethylpentane ethylbenzene

4.97 2.81

8.88 5.02

2,3,4-trimethylpentane 1,2,3-trimethylbenzene

10.58 10.01

5.00 4.73

ethene 1-butene

22.05 11.98

11.50 6.25

5 6

isoprene ethene

2.47 2.45

4.41 4.38

m-ethyltoluene 1,2,4-trimethylbenzene

9.68 9.28

4.58 4.39

1,2,3-trimethylbenzene 2,3,4-trimethylpentane

7.96 7.95

4.15 4.15

7 8

2,3-dimethylbutane m-ethyltoluene

1.66 1.47

2.96 2.64

1-butene m/p-xylene

6.43 6.42

3.04 3.04

ethylbenzene 1,2,4-trimethylbenzene

7.58 7.34

3.95 3.83

9

isobutane

1.36

2.43

ethylbenzene

6.06

10

1-butene

1.35

2.41

p-ethyltoluene

5.00

RI

PT

Rank

m/p-xylene

7.33

3.82

2.36

propene

5.00

2.61

AC

CE

PT E

D

MA

NU

SC

2.87

25

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Table 5. Summary of O3 photochemical sensitivity type (ZH) for SPM and RIR Transition region

1,2*,3,4*,5,7*,8*,9*,13,14,15*,16*,18*,

6,10*,11*,17,25*,29*

12,26,27,30,31

NOx-limited

2*,3,14*

1*,4,5*,13*

6,7,8*,9*,10*,11*,12,15

1,2*,4*,6,7*,8*,9*,15*,16*,18*,

3,5,10*,11*,13,20,24,

17

19*,21*,22*,23*,26,27,28*

25*,29*,30,31

2*,14*,15

1*,5*,6,7,13*

19*,20,21*,22*,23*,24, 28*,30,31

August July RIR (41days) August

AC

CE

PT E

D

MA

NU

SC

RI

* Dates with same O3-VOCs-NOx sensitivity conclusion of SPM and RIR.

PT

SPM (46days)

July

VOCs-limited

26

8*,9*,10*,11*

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Table 6 Summary of O3-VOCs-NOx sensitivity studies using different methods in China reference

Lanzhou (2 sites)

summer 2013

urban & suburban

VOCs-limited (urban) NOx-limited (suburban)

alkene & aromatic

Jia et al.(2016)

Shanghai

Jun 2006-Jun 2007

urban

VOCs-limited

aromatics

Ran et al.(2009)

Guangzhou

Jun 2011-May 2012

suburban

VOCs-limited (morning) to NOx-limited (afternoon)

aromatics

Zou et al. (2015)

Dingshanhu

Oct-Nov 2008

suburban

VOCs-limited

-

Sun et al.(2010)

Beijing

Aug-Sep 2008

urban

VOCs-limited

-

Sun et al.(2011)

Southern Taiwan (3 sites)

2003-2004

urban & rural

VOCs-limited (urban), NOx-limited (rural)

-

Peng et al.(2011)

VOCs-limited

reactive aromatics

Zhang et al.(2007)

VOCs-limited

-

Lu et al.(2010)

VOCs-limited

alkene

An et al.(2015)

VOCs-limited & transition alternatively

reactive alkene

This study

Hangzhou

summer 2006 Jun-Aug 2013 Jul-Aug 2013

urban

RI

SC

urban & suburban urban & suburban urban & suburban

Oct-Dec 2002

NU

Hong Kong (5 sites) Beijing (2 sites) Nanjing (4 sites)

PT

ozone major contributor

MA

VOCs/NOx, SPM,RIR

ozone control region

D

RIR

site type

PT E

SPM

observation period

CE

VOCs/NOx ratio

site location

AC

method

27

SC

RI

PT

ACCEPTED MANUSCRIPT

AC

CE

PT E

D

MA

NU

Fig. 1. Detailed geographical distribution of three observation sites in Hangzhou.

28

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

MA

Fig. 2. Time series of the hourly average data for (a) temperature and relative humidity, (b) atmospheric pressure and precipitation, and (c) wind direction and wind speed during the

AC

CE

PT E

D

observation period in Hangzhou. Vertical dotted lines indicate the zero hour for each day.

29

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

AC

CE

PT E

D

MA

Fig. 3. Wind frequency rose for July 1, 2013–August 15, 2013.

30

D

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

PT E

Fig. 4. Time series of the average hourly data for (a) O3, (b) NO2, (c) NO, (d) CO and (e) VOCs at three sites during the observation period. The horizontal line in Fig. (a) denotes the hourly average Chinese national air

AC

CE

quality standard for O3 of 200 µg/m3. Vertical dotted lines indicate the zero hour for each day.

31

PT E

D

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

Fig. 5. The average diurnal variations of O3, Ox, CO, NO2, NO, and VOCs at three sites during the observation

AC

CE

period.

32

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

Fig. 6 Dependence of O3 mean concentrations (ZH) on (a,b) temperature and (c,d) relative humidity. The error

AC

CE

PT E

D

bar represents standard deviation.

33

SC

RI

PT

ACCEPTED MANUSCRIPT

Fig. 7. Wind frequency plots of O3 concentrations (ZH) for (a) daytime and (b) nighttime. The frequency of

AC

CE

PT E

D

MA

NU

wind direction was divided into five O3 concentration ranges: 0–49, 50–99,100–149,150–199, and ≥200 µg/m3.

34

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

Fig. 8. Dependence of O3 mean concentrations (ZH) on wind speed during different periods of the day.

AC

CE

PT E

D

MA

The error bar represents standard deviation.

35

PT E

D

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

Fig. 9. Wind frequency plots of O3 concentrations (ZH) for three O3 episode periods (a–c) and one non-episode

AC

CE

period (d).

36

RI

PT

ACCEPTED MANUSCRIPT

AC

CE

PT E

D

MA

NU

SC

Fig.10. Fraction of VOCs category for Volume Mixing Ratio (VMR, in ppbv), Prop-Equiv (in ppbC) and Ozone Formation Potential (OFP, in ppbv) methods for different periods.

37

MA

NU

SC

RI

PT

ACCEPTED MANUSCRIPT

Fig. 11. Early morning (6:00–9:00), noontime (11:00-14:00) and late afternoon (16:00-19:00) VOCs/NOx (ppbC/ppbv) ratios (a–c) and average diurnal variation of VOCs/NOx ratios (d) in urban Hangzhou (ZH) during

D

the observation period. The ratios were divided into four daily maximum O3 concentration ranges: 0–100, 100–

AC

CE

PT E

200, 200–300, 300–400 µg/m3.

38

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Episode Ⅰ Non-episode

1.2

400

(a)

300

0.6

200

0.3

100

0.0

0

1.5 1.0

RIR (%/%)

500

E(t) O3 1h-max

(b)

0.5 0.0

PT

E(t)

0.9

Episode Ⅲ

-0.5 -1.0 -1.5

(c)

RI

1.2 0.8 0.4 0.0 -0.4

2013/7/7

2013/7/14

2013/7/21

2013/7/28

2013/8/4

NU

2013/6/30

AVOC BVOC CO NO

PARP OLEP ETHP TOL XYL

SC

RIR (%/%)

O3 1h-max (μg/m3)

1.5

Episode Ⅱ

2013/8/11

Fig. 12. Time series of E(t), O3 1h-max (a), daily-RIR of AVOC, BVOC, CO, NO (b) and PARP, OLEP, ETHP,

MA

TOL, XYL (c) for the urban site (ZH) during the observation period. E(t) was calculated using daily maximum

AC

CE

PT E

D

of 1/2 *{E(t)_NOx+E(t)_NOy}.

39

ACCEPTED MANUSCRIPT

Highlights 

Detailed ozone and its precursors characteristics were presented in Hangzhou, China.



Meteorological effects such as high temperatures, low relative humidity, easterly wind and



PT

low wind speed contribute to urban ozone episodes. Deep comparison between three ozone episode periods and one non-episode period was

VOCs/NOx ratio method, Smog Production Model (SPM) and Relative Incremental

SC



RI

made.

Summer ozone in urban Hangzhou mostly presents “VOCs-limited and transition region

CE

PT E

D

MA

alternately” characteristic.

AC



NU

Reactivity (RIR) were jointly performed to assess O3-VOCs-NOx sensitivity.

40