Ground-level ozone in Mekong delta region: precursors, meteorological factors, and regional transport

doi:10.21203/rs.3.rs-1520119/v1

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Ground-level ozone in Mekong delta region: precursors, meteorological factors, and regional transport

https://doi.org/10.21203/rs.3.rs-1520119/v1

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The Mekong Delta region (MDR), also known as Vietnam's rice bowl, produced a bountiful harvest of about 23.8 million tons in 2020, accounting for 55.7% of the country's total production, providing food security for 20% of the world population. With the rapid pace of industrialisation and urbanisation, concentration of ozone in the lower atmosphere has risen to a level that reduces crop yields, especially rice, and is, therefore, the subject of research. This study has a goals to simulate the spatio–temporal distribution of ground-level ozone in the area, also to evaluate the impact of precursor emissions and meteorological factors on spatio–temporal distributions of concentration. The study area was divided into six agro-ecological zones to clarify the role of emissions in each zone. The simulation results showed that the ground-level O₃ in the MDR ranged from 40.39 µg/m³ to 52.13 µg/m³. In six agro-ecological zones, the average annual ground-level O₃ concentration was relatively high, and was the highest in areas six (CZ) and seven (CPZ), with 46.11 µg/m³ and 46.41 µg/m³, respectively. In each zone, the annual average O₃ concentration tended to gradually increase from the inner delta to coastal areas. Two types of precursors, NO_x and NMVOCs, are the main contributors to O₃ pollution, with the largest contribution coming from zone 1 (FAZ) with 91.5 thousand tons of NO_x/year and 455.2 thousand tons of NMVOCs/year. Among the meteorological factors considered, relative humidity (RH) and surface pressure (P) were the two main factors that contributed to the increase in ground-level ozone. The spatio–temporal distribution of ground-level O₃ in the MDR was influenced by emission precursors from different zones as well as meteorological factors. The present results can help policymakers formulate plans for agro-industrial development in the entire region.

Mekong delta

Ozone

Emission precursors

Meteorological factors

WRF/CMAQ

Although, ground-level ozone is essential in the Earth's upper atmosphere, its presence in the lower atmosphere acts as a secondary air pollutant which negatively impacts human health and crops (Wilkinson et al., 2012). The effect of phytotoxic ozone concentrations on crop yields has been extensively studied (Pleijel, 2011; Arshad, 2021; Ramya et al., 2021; Wang et al., 2021; Shang et al., 2022). Europe’s economic loss in 2000 was estimated at €6.7 billion, based on the effects of ground-level ozone on 23 crops, (Ene et al., 2015); losses in global crop production for the same year were estimated at $11–18 billion USD (Avnery et al., 2011). Recent studies have shown that rice crops are also sensitive to ground-level O₃, especially in Asian cultivars (Shang et al., 2022). Several pilot studies conducted in Malaysia, Pakistan, and Vietnam have shown that the local rice varieties are affected by O₃ (Ishii et al., 2007; Arshad, 2021)

Ground-level ozone has long been recognized as a threat to crop health and human health, and studies on its effects have been conducted (Nouchi et al., 1991; Wahid et al., 1995; Brunekreef et al., 2002; Tang et al., 2014;). Plants that grow under favourable conditions form stomata on leaf surfaces to exchange gases for photosynthesis (Emberson et al., 2009). In this case, ozone is one of the gases that can penetrate plants. The reduction in crop yields due to the cumulative effects of ground-level ozone is evident when ozone “waves” are prolonged; if this period coincides with a significant increase in precursor emissions, the effect is doubled (Feng et al., 2009). Symptoms of injury can be observed through leaf wilt or necrosis on the leaf surface, which can coalesce to form larger areas of injury; the leaves often dry up and fall prematurely (Emberson et al., 2009). Ozone reduces grain size, weight, nutritional quality in field crops such as certain varieties of wheat, rice, maize, beans, and soybeans (Biswas et al., 2008; Decock et al., 2012)

Asian rice (Oryza sativa) is an important food crop, supplying more than half of the world's population, particularly in Asia and Africa (Doliente et al., 2021). The impact of ground-level ozone on local rice varieties in Asia may be of greater interest, given the important role played by agriculture in this region (Chen et al., 2008). Recent studies have shown that rice is sensitive to ground-level ozone, especially in Asian cultivars (Olszyk et al., 1997; Tang et al., 2014; Shang et al., 2022). Several pilot studies conducted in Malaysia, Pakistan, and Vietnam have shown that local rice varieties are affected by ground-level ozone (Wahid et al., 1995; Ishii et al., 2007). Therefore, it is important to assess the extent and scope of rice production loss due to ground-level ozone exposure, particularly in Southeast Asia, home to the world's two largest rice exporters, Thailand and Vietnam (Emberson et al., 2009). Even though people worldwide depend on rice grown in the MDR, there are few quantitative studies on ground-level ozone distribution (Ishii et al., 2004, 2007; Cha-um et al., 2007; Chen et al., 2008;Yamaguchi et al., 2014).

The Mekong Delta region (MDR) of Vietnam has an area of nearly 40,000 km² and a population of nearly 18 million, accounting for approximately 12% of the land area and 22% of the country's population. This is a land with a strategic location in Vietnam, bordering the East Sea and Southwest Sea on three sides, with a coastline of more than 700 km with 360,000 km², and an exclusive economic zone in a strategic location that is very favourable for economic development (Nguyen et al., 2019; Tran et al., 2019). The MDR, also known as the "rice bowl" of Vietnam, produced a bountiful yield of around 23.8 million tons in 2020, accounting for 55.7% of the country's total yields (Wassmann et al., 2019; Tu et al., 2021; Bich et al., 2022; Ferrer et al., 2022), providing food security for 20% of the world's population, particularly in regions where rice is the staple diet. With accelerated industrialization and urbanization of the MDR's, the concentration of ground-level ozone in the lower atmosphere has risen to levels that have resulted in reduced agricultural yields, particularly of rice; it is therefore the focus of this research (Danh et al., 2016).

Ground-level ozone is produced by nitrogen oxides (NO_x) and volatile organic compounds (VOCs) through photochemical reactions (Emberson et al., 2009; Guo et al., 2019); therefore ground-level ozone dispersion was simulated through the application of photochemical transport and meteorological models (Hogrefe et al., 2000; Sokhi et al., 2006; Davis et al., 2011; Li et al., 2014; Wang et al., 2015; Astitha et al., 2017; Trieu et al., 2017; Wang et al., 2017; Chen et al., 2021; Mousavinezhad et al., 2021; Wang et al., 2021). The using meteorological/ photochemical transport models to predict hourly ground-level ozone concentrations, and the modelling results were evaluated based on the measured field data were done in a various studies (Sokhi et al., 2006; Davis et al., 2011; Li et al., 2014; Wang et al., 2015).

Ground-level ozone concentrations often occur on days with strong sunlight and weak winds, creating favourable conditions for the production and accumulation of ozone and its precursors (Wang et al., 2017). Wind direction was also important because it affects the transport of pollution, thereby increasing the temperature of windy sites (Epa, 2006; Li et al., 2014; Mitchell et al., 2021). The quantification of ozone production from different precursors, including NO_X, VOCs, and individual VOC species, was performed (Garner et al., 2013; Simon et al., 2015; Wang et al., 2015). Thus, with the help of these models, it was possible to propose solutions to reduce ozone pollution by minimising precursor emissions (Xue et al., 2014; Wang et al., 2017).

Favourable meteorological conditions for photochemical episodes have been extensively studied (Camalier et al., 2007; Simon et al., 2012; McNider et al., 2020). For example, sunny weather and low wind velocity lead to pollution accumulation and O₃ production (Xue et al., 2014; Carter et al., 2017). Several studies have been conducted to determine the relationship between ozone concentration and local meteorological parameters, including solar irradiance, temperature, relative humidity, wind speed and direction, and cloud cover (Wang et al., 2017).

Given the special importance of the Mekong Delta to world food security, this study was conducted with the goal of applying WRF/CMAQ in combination with MLR models, on the one hand simulating the spatio–temporal distribution of ground-level ozone in the region, and on the other hand evaluating the impact of precursor emissions and meteorological factors on the spatio–temporal distribution variation of ground-level ozone. The results allow the formulation of long-term and medium-term plans for socio-economic development, coupled with environmental protection, with attention to costs, economic benefits, and environmental efficiency.

2.1 Study area

The Mekong Delta Region (MDR) consists of 13 provinces (Fig. 1). According to data from the General Statistics Office of Vietnam in 2019, the Mekong River Delta has the largest total area in Vietnam, with a higher economic growth rate than the whole country. Rice farms alone account for 47% of the area and 56% of the country's rice production, and rice exports from the entire region account for 90% of production (GSO, 2020; Cramb, 2020).

Based on geographical, agricultural, water resource features, the MDR can be classified into six agro-ecological zones, excluding hills and mountains (Nguyen et al., 2007). These areas are affected by flooding or saline intrusion, as indicated by the demarcation lines in Fig. 1 (Nguyen et al., 2007). These six zones include the freshwater alluvial zone (FAZ), which is located along the Trans-Bassac and Mekong rivers in the central part of the delta, and is characterised by alluvial soils, covering an area of approximately 900,000 ha of available fresh water. In this zone, people make a living by diversifying agricultural activities, practising two- or three-crop rice farming combined with the cultivation of fruit trees, vegetables, and aquaculture. Next is the Dong Thap Muoi zone (PRZ), located in Dong Thap province and part of Long An province, with an area of approximately 500,000 ha. This is the upper part of the delta with 0.5 m lower than average sea level. The area had alkaline soil, and water and related environmental factors were partially controlled by flooding and acid toxicity. People in this area make a living with rice farming and integrated aquaculture. The third zone is the Long Xuyen-Ha Tien Quadrangle (LXZ), located between the two provinces of An Giang and Kien Giang, with an area of 400,000 ha, and is also characterised by alkaline soil. Water and environmental factors changed from salty alum ecology to freshwater ecology after 2000. Rice is the main crop in the area, and rice-based farming is the main activity. The fourth area is the Trans-Bassac Depression Zone (TBZ), located to the west of Can Tho, covering an area of approximately 600,000 ha. This is a low-lying area on a plain that is not seriously affected by floods and saline intrusion and has favourable conditions for intensive farming and diverse crop production. Residents make a living by cultivating two or three rice crops. The coastal zone (CZ), located along the eastern part of the Mekong Delta, covers approximately 600,000 ha. Large areas in this zone have acidic soil. Since 1998, parts of this region have been subject to changing water and environmental conditions from brackish to permanent freshwater ecology. In this area, people earn a living by farming shrimp and growing rice. The Ca Mau Peninsula (CPZ), located at the southernmost tip of the delta, covers an area of approximately 800,000 ha. The area is characterised by seasonal salinisation of the soil and various rice-based farming systems in terms of rainwater use. Since 1998, large swaths of the region have undergone changes in water and environmental conditions to a permanent freshwater eco-environment, with shrimp farming being the dominant activity here (Nguyen et al., 2007).

Figure 1. Study area

2.2 Data

2.2.1 Meteorology

In this study, the hourly realistic meteorological data from the observation stations of Can Tho and Tien Giang (Fig. 2) in the freshwater alluvial area and Kien Giang in the lowland area were extracted in March and October 2018 representing the dry and wet seasons of the MDR from the websites of https://www.wunderground.com/ and https://www.timeanddate.com/, respectively. A time-series dataset of temperature, wind speed, wind direction, relative humidity, and surface pressure was captured from each monitoring site, which was used to verify the results of the WRF simulations in terms of accuracy and error levels using statistical indicators.

Figure 2. The measuring sites of ground-level O₃ and meteorological parameters

2.2.2 Observation of ground-level O₃

In 2018, the field-measured ground-level O₃ concentration datasets were obtained from 11 monitoring sites, including N, NT1, DT1, DT3, DT4, DT6, GT2, GT3, CN1, CN2, and CN4 (Fig. 2), which had been managed and operated by the Binh Duong Center of Natural Resources and Environment-Technical Monitoring (BREM) (Binh Duong, 2019). These datasets were used to support the processes of calibration and validation of the ground-level O₃ concentration results simulated by the coupled WRF/CMAQ model. In addition, ground-level O₃ concentration datasets were obtained by manual and discontinuity measurements four times during the monitoring days (Fig. 2).

2.2.3 Emission inventory datasets

In this study, the usage emissions dataset named CAMS-GLOB-ANT was provided by Emissions of Atmospheric Compounds and Compilation of Ancillary Data (ECCAD) (https://eccad3.sedoo.fr/), developed by the Copernicus Atmosphere Monitoring Service, and published in 2018 (ECCAD, 2020; Granier et al., 2019). This is an output product from a combination of EDGARv4.3.2 emission data built by the Common European (Crippa et al., 2018) and the Community Emissions Data System (CEDS) emission inventory (Hoesly et al., 2018) providing emissions data for the next IPCC report, AR6.

The CAMS-GLOB-ANT dataset is a global emissions inventory dataset from a variety of sectors and is available for the period 2000–2020. Because the sectors contributing to the emissions of EDGAR v4.3.2 and CEDS are heterogeneous, the Copernicus Atmosphere Monitoring Service defines general global emission activity groups for the CAMS-GLOB-ANT dataset to ensure a match between the two data sources (Granier et al., 2019). The global emission flux of different substances in the CAMS-GLOB-ANT dataset was estimated from 14 sectors of anthropogenic emissions, including power generation¹ (ENE), road transportation² (TRO), off-road transportation³ (TNR), fugitives⁴ (FEF), industrial process⁵ (IND), solvents⁶ (SLV), ships⁷ (SHP), solid waste and wastewater⁸ (SWD), residential and other sectors⁹ (RES), agriculture livestock¹⁰ (AGL), agriculture soils¹¹ (AGS), agricultural waste burning¹² (AWB), general agriculture activities¹³ (AGR), and total contribution of sectors¹⁴ (SUM) (Granier et al., 2019).

The used dataset of inventory and emission load estimation, including 19 primary substances (EC, OC, PMC, PMOTHR, PNCOM, H₂O, K⁺, Na⁺, Ca²⁺, Al³⁺, Fe^2+/3+, Mg²⁺, Mn²⁺, Si⁴⁺, Ti²⁺, NH₄⁺, Cl^-, NO₃^-, and và SO₄^2-) and 34 substances joining in secondary reactions (NO_x, SO₂, NH₃, CO, CO₂, CH₄, BC, OC, NMVOCs, and 25 other VOCs species) contribute to ground O₃ formation encoded according to the Carbon Bond 05 chemical mechanism (Sarwar et al., 2008). The inventory emission dataset was generated by monthly averages with a spatial resolution of 0.1^o × 0.1^o (Granier et al., 2019). Emissions of different VOC species were also available in the global emission dataset of CAMS-GLOB-ANT according to each VOC-contributing sector (Granier et al., 2019). In this study, the CAMS-GLOB-ANT global dataset, version V2.1 created for the 2000–2020 period, (Granier et al., 2019) was used to analyse anthropogenic emission contributions in the Mekong Delta region. The results of emission data processing are shown in the Fig. S1 – S36.

2.3 WRF/CMAQ

2.3.1 Nested domain setup in the coupled WRF/CMAQ

The model of WRF (Weather Research and Forecasting) with version 3.8 (Skamarock et al., 2008) combined with the Community Multiscale Air Quality Modelling System (CMAQ) ver. 5.2.1 were used by Borge et al. (2014), Hu et al. (2015), and Lang et al. (2017) to simulate ground-level ozone (O₃) in Southern Vietnam between 01 January, 2018, and 31 December, 2018.

Two level nested simulation calculation domains, D01 and D02, were set up in the WRF/CMAQ coupled models, where computational domain D01 had spatial resolution of approximately ~ 30.43 km × 30.43 km, and was the largest domain (76 columns and 94 rows; the area of D01 was approximately 5.41 × 106 km²). Domain D02 had a spatial resolution of approximately 9.55 km × 9.55 km was the second domain nested in the D01 domain (55 columns and 43 rows; the area of the D02 domain was about ~ 2.11 × 105 km²). The details of the specifications of calculation domains D01 and D02 are shown in Table 1. Domain D01 covers all of Vietnam, while D02 consists of most of the southern provinces, including the Mekong Delta provinces (or the study area), the Southeast, and part of the South Central and Central highlands provinces, which might have the most significant influence on the research area. The boundary conditions of D02 were determined according to D01. The boundary conditions of the largest domain, D01 were provided by the default configuration available in the CMAQ software. The hourly meteorological fields according to the input requirements of the CMAQ model were simulated by a WRF model using data from the National Center for Environmental Prediction (NCEP) Final (FNL) Operational Global Analysis (https://rda.ucar.edu/datasets/ds083.2/) (NCEP, 2000) and were considered as the original meteorological conditions and boundary meteorological conditions for the coupled model.

Table 1

Setup of the computing spatial domains for ground-level O₃ simulations by the coupled WRF/CMAQ models
Domain parameters	Domain D01	Domain D02
Domain range	Vietnam	Southern provinces of Vietnam (Study area)
Grid size, X [km] × Y [km]	2,312.68 × 2,860.42	525.25 × 410.65
Số nút lưới tính, N_x × N_y	75 × 93	54 × 42
Total numer of grid size	7,144	2,365
Grid size (km)	30.43	9.55
Center coordinate	(13.3826^o; 104.969^o)	(10.1195^o; 105.955^o)
Coordinate system	Asia Lambert Conformal Conic	Asia Lambert Conformal Conic
Setup domain type	The nested modelling domain	The nested modelling domain

Table 1. Setup of the computing spatial domains for ground-level O₃ simulations by the coupled WRF/CMAQ model

2.3. 2 WRF/CMAQ modelling performance evaluation

In this study, the fusion data method between the measured and simulated ground-level O₃ results was used to correct the initial outcomes estimated by the coupled WRF/CMAQ model based on the study outcomes of Friberg et al., (2016) and Senthilkumar et al., (2019). Eq. (1) illustrates the calculation of two regression parameters, α and β, reflecting the correlation relationship applied to correct the computed ground-level O₃ concentration results by the coupled WRF/CMAQ model as follows:

$$CMA{Q_{x,Corrected}}=\alpha CMA{Q_x}^{\beta }$$

where CMAQ_x is the exported initial ground-level O₃ concentration based on the computing results of the coupled WRF/CMAQ model at the observing stations x of the given time t (h); CMAQ_{x, Corrected} is the verified ground-level O₃ concentration close to the field measured data in the monitoring sites x of the specific time t (h), and α and β are the coefficients of the correlation equation used for calibration.

The outcomes of the calculated ground-level O₃ concentration by WRF/CMAQ after using the corrected functional equation were verified according to the measured O₃ concentration at the observation stations in Binh Duong Province. Based on statistical indexes for evaluating the simulation capabilities of the coupled WRF/CMAQ model, which includes the coefficients of the Nash-Sutcliffe Efficiency (NSE) (2), other statistical indices such as the mean bias (MB) (3), root mean squared error (RMSE) (4), normalised mean bias (NMB) (5), normalised mean gross Error (NME) (6), and the correlation coefficient R (7) were estimated as follows (Wang et al., 2021):

$$NSE(Nash)=1 - \frac{{\sum\limits_{1}^{N} {{{({M_i} - {O_i})}^2}} }}{{\sum\limits_{1}^{N} {{{({O_i} - \overline {{{O_i}}} )}^2}} }}$$

$$MB=\frac{1}{N}\sum\limits_{{i=1}}^{N} {({M_i} - {O_i})}$$

$$RMSE=\sqrt {\frac{1}{N}\sum\limits_{1}^{N} {{{({M_i} - {O_i})}^2}} }$$

$$NMB=\frac{{\sum\nolimits_{1}^{N} {({M_i} - {O_i})} }}{{\sum\nolimits_{1}^{N} {({O_i})} }} \times 100$$

$$NME=\frac{{\sum\nolimits_{1}^{N} {\left| {{M_i} - {O_i}} \right|} }}{{\sum\nolimits_{1}^{N} {({O_i})} }} \times 100$$

$$R=\frac{{\sum\limits_{{i=1}}^{N} {\left[ {({M_i} - \overline {{{M_i}}} ) \times ({O_i} - \overline {{{O_i}}} )} \right]} }}{{\sqrt {\sum\limits_{{i=1}}^{N} {{{({M_i} - \overline {{{O_i}}} )}^2}} } \times \sqrt {\sum\limits_{{i=1}}^{N} {{{({O_i} - \overline {{{O_i}}} )}^2}} } }}$$

where ${M_i}$ is the result of the ground-level O₃ concentration simulated by the combined WRF/CMAQ models, $\overline {{{M_i}}}$ is the average ground-level O₃ software simulation result, ${O_i}$ is the result of ground-level O₃ concentrations from five measuring stations in the districts of Binh Duong Province (in domain D02), $\overline {{{O_i}}}$is the average ground-level O₃ result from the monitoring stations in the study area, and N is the size of the validated sample.

Calibration equations 9 and 19 were built according to (1) from the simulated results extracted at the ground-level O₃ measuring stations (Fig. 2). Coefficients α and β were determined based on the difference between the actual measured and modelled datasets.

Accuracy verifications of the estimated results using the coupled WRF/CMAQ model based on statistical indicators (2)–(7) and Table 2. The NSE values in the three field-measured sites of CN1, NT1, and CN4 were NSE_CN1=0.918, NSE_NT1=0.987, and NSE_CN4=0.928, respectively, reaching a good level; however, the values of station DT1 achieved a good level with NSE_DT1 = 0.888 (with NSE > 0.800), and station GT2 also had a slightly good level with NSE_GT2 = 0.729 (with NSE > 0.700). Furthermore, other statistical indexes also showed that the simulation capability of the coupled WRF/CMAQ model was from a good level to an excellent level. Specifically, the RMSE and MB indices of measuring station DT1 were 4.648 and 0.537, respectively; those of observation station GT2 were 23.330 and − 0.540, respectively; those of monitoring sites CN1 were 3.943 and 0.127, respectively; those of measuring station NT1 were 1.891 and 0.464, respectively; those of monitoring site CN4 were 4.419 and − 0.091, respectively. Similarly, other indices consisting of NMB, NME, and the calculated correlation coefficient R in the above 5 measuring stations all met the given allowable conditions; specifically, these values of the measuring sites were respectively as follows: NMB_DT1=2.254%; NMB_GT2=-0.669%; NMB_CN1=0.568%; NMB_NT1=0.267%; NMB_CN4=-0.362% (with the criteria of -30% <NMB < 30%); NME_DT1=13.060%; NME_GT2=13.440%; NME_CN1=12.635%; NME_NT1=6.998%; and NME_CN4=10.697% (with the criteria of NME < 50%) and the correlation coefficients of R_DT1=0.944; R_GT2=0.856; R_CN1=0.959; R_NT1=0.995, and R_CN4=0.963 (with the criteria of R > 0.5) (Table 2). The simulated ground-level O₃ concentration calibration equations established for each month of 2018 are shown in Table 3.

Table 2

Recommended benchmarks for ground-level O₃ concentration modelling by the coupled WRF/CMAQ model in this study
The statistical indexes	Range of value	Benchmarks	References
NSE (Nash)	(-$\infty$; +1]	0.40–0.65: (GSO, 2020)Pass 0.65–0.85: Good > 0.85: Excellent	(McCuen et al., 2006; Xiaohui Zhong et al., 2015; Knoben et al., 2019)
MB (Mean Bias)	(-$\infty$; +$\infty$)	-	(Morris et al., 2005; Eder et al. 2006; Emery et al., 2017; Nguyen et al., 2020)
RMSE (Root Mean Squared Error)	(-$\infty$; +$\infty$)	-	(Morris et al., 2005; Eder et al., 2006; Emery et al., 2017; Nguyen et al., 2020)
NMB (Normalised Mean Bias)	(-100%; +$\infty$)	-30% < NMB < + 30%	(Morris et al., 2005; Eder et al., 2006; Emery et al., 2017; Nguyen et al., 2020),
NME (Normalised Mean Gross Error)	(0%; +$\infty$)	< 50%	(Morris et al., 2005; Eder et al. 2006; Emery et al., 2017; Nguyen et al., 2020)
Correlation coefficient R	[-1; +1]	R < 0.40: Not pass 0.40 < R < 0.80: Pass 0.80 < R < 0.85: Good R > 0.85: Excellent	(Emery et al., 2017; Nguyen et al., 2020)

Table 3

Equation to calibration ground - level O3 concentration by month, 2018
Month	Calibrated equations for ground- level ozone concentration O₃	Month	Calibrated equations for ground- level ozone concentration O₃
1	y = 1.0332 x^0.9814 (R² = 0.9844) (8) α= 1.0332 and β = 0.9814 (0 < β < 1)	7	y = 1.07227 x^0.9758 (R² = 0.9976) (14) α = 1.07227 and β = 0.9758 (0 < β < 1)
2	y = 0.9666 x^0.9924 (R² = 0.9983) (9) α = 0.9666 and β = 0.9924 (0 < β < 1)	8	y = 1.0156 x^0.9882 (R² = 0.9938) (15) α = 1.0156 and β = 0.9882 (0 < β < 1)
3	y = 1.0985 x^0.9742 (R² = 0.9641) (10) α = 1.0985 and β = 0.9742 (0 < β < 1)	9	y = 0.9845x^0.9961 (R² = 0.9886) (16) α = 0.9845 and β = 0.9961 (0 < β < 1)
4	y = 1.0985 x^0.9742 (R² = 0.9943) (11) α = 1.0985 and β = 0.9742 (0 < β < 1)	10	y = 1.0473 x^0.9855 (R² = 0.9317) (17) α = 1.0473 and β = 0.9855 (0 < β < 1)
5	y = 0.9589 x^0.9882 (R² = 0.9714) (12) α = 0.9589 and β = 0.9882 (0 < β < 1)	11	y = 0.9437x^0.9975 (R² = 0.9969) (18) α = 0.9437 and β = 0.9975 (0 < β < 1)
6	y = 1.0799 x^0.9895 (R² = 0.9937) (13) α = 1.0799 and β = 0.9895 (0 < β < 1)	12	y = 1.0745 x^0.9891 (R² = 0.9851) (19) α = 1.0745 and β = 0.9891 (0 < β < 1)

Table 2. Recommended benchmarks for applying ground-level O3 concentration modelling by the coupled WRF/CMAQ model

Table 3. Equation to calibration ground - level O3 concentration by month, 2018

2.4 Multiple linear regression

Multiple linear regressions (MLR) (Amos P.K. Tai et al., 2010, 2012) were established to clarify the multiple linear correlations of the ground-level O₃ concentration with meteorological variables (i), precursor emission variables (ii) and both types of the above-mentioned variables (iii). The hourly and daily average ground-level O₃ concentrations correspond to meteorological data and precursor emissions, respectively. Detailed MLR models were built in the form of Eq. 20, as follows:

$${y_{i,j}}=\alpha +{\beta _1}{x_1}+{\beta _2}{x_2}+ \ldots +{\beta _{k - 1}}{x_{k - 1}}+{\beta _k}{x_k}+\varepsilon$$

where α is the cut-off point on the vertical axis, β, β₁, β₂,…, β_k−1 and β_k are the slopes or regression coefficients (parameters), and ε is a random variable (an error term). First, the (20) was considered for three cases, in which case (i) presents the relationship between the ground-level O₃ concentration (C_O3) and three meteorological factors: temperature x₁ (T), relative humidity of x₂ (RH), and surface pressure (referred to as pressure) x₃ (P); in other words, it was crucial to generate a correlation function of C_O3 = F (T, RH, and P). The next case (ii) considered the correlations between ground-level O₃ concentration (C_O3) and five types of precursor emissions, specifically, the CH₄ emission factor was E_CH4, x₁; the CO emission factor was E_CO, x₂; the NO_x emission factor was E_NOx, x₃; the NMVOCs emission factor was E_NMVOCs, x₄; other VOCs emission factors were E_Other−VOCs, x₅, or C_O3=F (E_CH4, E_CO, E_NOx, E_NMVOCs, and E_Other−VOCs) for six zones of the MDR (corresponding to six agro-ecological zones). Finally, case (iii) obtained the correlation of C_O3=F (T, RH, P, E_CH4, E_CO, E_NOx, E_NMVOCs, and E_Other−VOCs). The obtained fundamental results were linear regression equations showing the multivariable correlation between ground-level O₃ concentration and meteorological variables (x₁, x₂, x₃) and precursor emissions (x₄, x₅, x₆, x₇, x₈). MLR models were created for six zones of the MDR, namely FAZ, PRZ, LXZ, TBZ, CZ, and CPZ (except for zone four, which was HMZ). For cases (i), (ii), and (iii), the k values were 3, 5, and 8, respectively; j is the correspondence for each area according to the zoning in the MDR (j corresponds to the agro-ecological zones, i = one to three, and five to seven; excluding i = four (for which we did not estimate emission values because of the hill and mountainous areas); i = 1 to 3 for cases (i), (ii), and (iii).

2.5 Meteorological modelling

During the dry season, the wind direction at all three meteorological observation sites followed the same patterns in Can Tho, Kien Giang, and Tien Giang (Fig. 2); The dominant direction of the wind was southeast, and the wind speed ranged from 3.3 m/s to 5.5 m/s (considered a light breeze). In the wet season, the wind speed ranged between 5.5 m/s and 7.9 m/s (considered a moderate breeze), and the dominant direction of the wind was also southwest. Furthermore, in the wet season at the two measuring stations in Kien Giang and Tien Giang, the northeast wind direction was observed with a high frequency and the wind speed ranged from 3.3 m/s to 5.5 m/s (considered as the light breeze level).

The simulated meteorological parameter results also showed that the relative humidity in the wet season was higher than that in the dry season at all times. At the Can Tho station, the relative humidity in the dry season was approximately 68.3%, whereas in the wet season, this value increased by 15.0%, reaching approximately 83.5%. Among the three measuring stations, the Can Tho station observed a larger difference than the other two stations; the humidity difference of the Can Tho, Kien Giang, and Tien Giang stations obtained was 15.2%, 12.9%, and 12.4%, respectively. In contrast to the relative humidity factor, the surface pressure during the wet season was higher during the wet season, but the difference was not high, ranging from 0.2 to 0.3 mbar. The monthly mean surface pressure in both seasons showed a declining trend at the measuring stations of Can Tho, Kien Giang, and Tien Giang (Fig. 2).

The WRF simulation was validated based on the field-measured outcomes, as shown in Table S1. The meteorological parameters of temperature, wind speed, wind direction, relative humidity, and surface pressure were used for the evaluation processes. In particular, the statistical indicators showed that the simulated pressure factor had high accuracy, with an RMSE value between 1.28 and 2.00. The Tien Giang station observed the values of wind speed (in both seasons) and wind direction in the dry season with errors within an allowable limit. The RMSE index values of the wind speed factor at these stations were 1.6 and 1.7, respectively, for the dry and wet seasons. The MB index value of the wind speed factor in the wet season was 0.11, and the wind direction factor in the dry season was approximately 5.18. The temperature parameter obtained an acceptable error level with the RMSE index values ranged from 2.19 to 2.80, whereas the values of the relative humidity parameter had an average error level, ranging from 9.05 to 16.38. Among the five considered meteorological parameters, the wind direction parameter estimated a relatively high error level than the other parameters, with the RMSE index value ranging between 74.54 and 159.15.

The estimated meteorological factor results consisting of the temperature, relative humidity, and surface pressure used to assess the study area were also applied in the CMAQ model shown in Figures S37–S39.

2.6 Implementation steps

The Information/data-model system and processing steps used in this study are shown in Figure 3. The steps for processing the emission data are described in Section 2.2.3. The steps of processing the results and creating a meteorological dataset using the WRF model are described in Section 2.3.1. The field measured data of the meteorological and ozone measurements are shown in Sections 2.2.1 and 2.2.2. The steps for calibrating and validating the CMAQ model are presented in Section 2.3. The MLR model determined the relationship between O₃ concentration, emissions, and meteorology, as shown in Section 2.4. To evaluate the accuracy of the meteorological simulation results are presented in Section 2.5, the concentration simulation are presented in Section 2.3.

Figure 3. Implementation steps

3.1 Spatio - temporal distributions of ground-level ozone concentration

The simulation results over the entire MDR showed that the ground-level ozone concentration in the region ranges from 40.39 µg/m³to 52.13 µg/m³. In zone one (FAZ), O₃ concentrations ranged from 40.45 µg/m³ to 49.63 µg/m³ ; in zone two (PRZ) this value ranges from 40.39–43.11 µg/m³ ; in zone three (LXZ) in the range 42.13–48.01 µg/m³ ; in zone four (HMZ) the concentration ranged from 42.39 µg/m³ to 44.77 µg/m³ ; in zone five (TBZ) between 41.42–47.02 µg/m³; in zone six (CZ) the concentration ranged from 42.65 µg/m³ to 50.22 µg/m³; in zone seven (CPZ) this value ranged from 42.85 – 52.13 µg/m³. However, in Vietnam's National Technical Regulations, there is no regulation for the annual average threshold of O₃ (QCVN 05:2013/BTNMT), based on simulation results, all seven zones above have a relatively high annual average ground-level O₃ concentration, of which the highest is in zones six(CZ) and seven (CPZ) with average concentrations of 46.11 µg/m³ and 46.41 µg/m³, respectively, zone three (LXZ) had an average concentration of 44.70 µg/m³ (1.04 times lower than that of CPZ); followed by zones four (HMZ) and five (TBZ) with average concentrations of 43.35 µg/m³ and 43.19 µg/m³ respectively (1.07 and 1.08 times lower than that if CPZ, respectively); and zones one (FAZ) and two (PRZ) had the lowest average concentrations across the entire MDR with 42.70 µg/m³ and 41.83 µg/m³ respectively (1.09 and 1.11 times lower than that of CPZ, respectively). In each zone, the average ground-level O₃ concentration tends to increase gradually from the inner delta to the coastal areas of the east and west coasts (especially the east coast and the southernmost at the tip of the South China Sea of Ca Mau province), from north to south, typically localities with concentrations > 48.5 µg/m³ concentrated mainly in part of Tien Giang, Ben Tre, Tra Vinh, Soc Trang, and Bac Lieu provinces (about the east, southeast, and south) of zone six (CZ); the eastern and south-eastern parts of Ca Mau and Bac Lieu provinces in zone seven (CPZ); and the western and southwestern parts of Kien Giang province in zone one FAZ (west coastal area). The map of ground-level O₃concentration by year and month is shown in Figures 3 and 4, respectively.

Figure 4. Spatio–temporal distribution of average ground-level O₃ concentration in 2018 in the Mekong Delta Region

Figure 5. Spatio–temporal distribution of average monthly ground-level O₃ concentrations in the MDR

3.2 Impacts of meteorological factors

The results of building a linear regression model between O₃ concentration (C_O3, y_i) and meteorological factors, including temperature x₁ (T, ^oK), relative humidity x₂ (RH, %), pressure surface pressure (or pressure for short) x₃ (P, Pa), and C_O3 = F (T, RH, and P), are shown in Table 4. Regression calculation results shows that the correlation between temperature x₁, relative humidity x₂, and pressure x₃ for the value of O₃ concentration in six areas of the Mekong Delta has the form of (20) – (25), as follows:

Table 4. Calibrated equations C_O3 = F (T, RH and P)

Table 4. Correlation equation C_O3 = F(T, RH and P)

The correlation coefficients for the three meteorological factors in the six zones are R_FAZ=0.417000, R_PRZ=0.42529, R_LXZ=0.31874, R_TBZ=0.36530, R_CZ=0.43631, and R_CPZ=0.36806. In all six zones of the MDR, the O₃ concentration values tended to increase to 3.0410 µg/m³, 3.2776 µg/m³, 3.2485 µg/m³, 2.7163 µg/m³, 4.2081 µg/m³ and 4,849 µg/m³ for every 1 unit (1 ^oK) increase in temperature (T), respectively (when relative humidity (RH) and surface pressure (P) remained unchanged). Similarly, for every 1 unit (1%) increase in RH, the O₃ concentration tends to increase by 0.6476 µg/m³, 0.4885 µg/m³, 0.6606 µg/m³, 0.8825 µg/m³, 0.6320 µg/m³, and 0.7786 µg/m3, respectively (when T and P do not change), while P also has a similar positive effect when for every 1 unit (1 Pa) of P changes in an increasing direction, the O₃concentration also tended to increase to 7.4043 µg/m³, 7.1928 µg/m³, 5.9871 µg/m3, 6.6471 µg/m³, 8.6748 µg/m³, and 7.7107 µg/m³, respectively (when RH and T remained unchanged). Thus, all three meteorological factors, temperature, relative humidity, and surface pressure contribute to the increase in O₃ concentration, in which surface pressure and temperature reflect the most significant impact of varying complexity depending on the geographical region.

Based on the results of the univariate analysis of each meteorological factor (Table S27), the correlation coefficient (R) of P was the highest in areas two (PRZ) and six (CZ) with an explanatory level of approximately 16.60% and 16.82%, respectively, and the R of T was highest in areas one (FAZ), six (CZ), and seven (CPZ).

The results of multivariable regression calculated at p-value show when considering all three meteorological factors mentioned above, are T, RH, and P for O₃ concentration in the above six zones of the Mekong Delta; only the relative humidity RH and surface pressure P were statistically significant for (1) zone one (FAZ), zone three (LXZ), and zone five (TBZ; and (2) all three factors above were statistically significant in zone two (PRZ), zone six (CZ), and zone seven (CPZ),. Thus, in zones one (FAZ), three (LXZ), and five (TBZ), there were at least two meteorological factors (relative humidity and surface pressure) that were independent with significant predictive significance value of O₃ concentration in these three zones. In zones two (PRZ), six (CZ), and seven (CPZ), all three meteorological factors above had a significant influence on the O₃ concentration in each zone (Table 5).

Table 5. Values in univariate linear regression analysis in 06 zone in the MDR of C_O3 = F(T), C_O3 = F(RH) và C_O3 = F(P)

Factors	α	β	R	R²	*p-value*
Agro-ecological zone one, FAZ
Temperature, T	1,105.1139	-3.5342	0.14782	0.02185	0.00465
Relative humidity , RH	42.6918	-0.0020	0.00058	0.000001	0.99118
Surface pressure, P	-5,907.9236	5.8983	0.39190	0.15359	0.00000
Agro-ecological zone two, PRZ
Temperature, T	361.0229	-1.0616	0.04037	0.00163	0.44195
Relative humidity , RH	72.0278	-0.4099	0.12546	0.01574	0.01648
Surface pressure, P	-5,844.6621	5.8349	0.40747	0.16603	0.00000
Agro-ecological zone three, LXZ
Temperature, T	820.1010	-2.5780	0.10060	0.01012	0.05484
Relative humidity , RH	34.5034	0.1346	0.03568	0.00127	0.49683
Surface pressure, P	-4,519.9515	4.5264	0.28588	0.08173	0.00000
Agro-ecological zone five, TBZ
Temperature, T	1,066.2555	-3.4042	0.13314	0.01773	0.01089
Relative humidity , RH	25.2459	0.2347	0.06487	0.00421	0.21634
Surface pressure, P	-4,785.1857	4.7847	0.30798	0.09485	0.00000
Agro-ecological zone six, CZ
Temperature, T	922.5689	-2.9204	0.11804	0.01393	0.02411
Relative humidity , RH	70.4457	-0.3128	0.06129	0.00376	0.24276
Surface pressure, P	-6,706.8905	6.6923	0.41012	0.16820	0.00000
Agro-ecological zone seven, CPZ
Temperature, T	948.9186	-3.0068	0.12607	0.01589	0.01596
Relative humidity , RH	29.0441	0.2193	0.06176	0.00381	0.23922
Surface pressure, P	-5,820.2431	5.8132	0.32643	0.10655	0.00000

Table 5. Values in univariate linear regression analysis in 06 zone in the MDR of C_O3 = F(T), C_O3 = F(RH) và C_O3 = F(P)

3.3 Impacts of precursor emission factors

The indicators for O₃ concentration were C_O3, and y_2,j; CH₄precursor emissions were E_CH4, x₁; CO was E_CO, x₂; NO_x was E_NOx, x₃; NMVOCs were E_NMVOCs, x₄; other VOCs were EO _ther-VOCs, x₅. The results of the multivariable regression equations C_O3=F (E_CH4, E_CO, E_NOx, E_NMVOCs, and E _Other-VOCs) (Table 6), show the multivariable correlation of precursor emissions (x₁, x₂, x₃, x_4, and x₅) and the value of O₃ concentration in the six zones of the Mekong Delta (y_2,j) has the form of (26)– (31), as follows:

Table 6. Multivariable correlation equation C_O3 = F (E_CH4, E_CO, E_NOx, E_NMVOCs , E_Other-VOCs)

From the equations in Table 6, common correlation coefficient in the six partitioned zones have the values of R₁=0.27185, R₂=0.54097, R₃=0.35830, R₄=0.27282, R₅= 0.38376, and R₆=0.25777, respectively. O₃ concentration values in zones two (PRZ), three (LXZ), five (TBZ) and six(CZ) tended to increase for every 1 unit (1 kg/day); the emissions of CH₄ or NO_x or NMVOCs or Other VOCs increased (when the remaining 04 precursors remained unchanged). Specifically, the O₃ concentration for E_CH4 increased by 0.7155 µg/m³, 0.2874 µg/m³, 0.0580 µg/m³, and 0.0707 µg/m³ respectively; the O₃ concentration for E_NOx increased by 15.7726 µg/m³, 4.2370 µg/m³, 0.8504 µg/m³, and 0.5965 µg/m³respectively. The O₃ concentrations for E_NMVOCs increased by 0.1063 µg/m³, 0.0181 µg/m³, 0.0086 µg/m³, and 0.0235 µg/m³ respectively; the O₃ concentrations for E_Other-VOCs, increased by 9,1465 µg/m³, 4.0573 µg/m³, 0.4920 µg/m³, and 0.5515 µg/m³, respectively. In contrast, the O₃ concentration in these four zones tended to decrease for every 1 unit (1 kg/day) of CO emissions increased (when the remaining four precursors remained unchanged); specifically, the O₃ concentration for E_CO decreased by 0.7555 µg/m³, 0.2472 µg/m³, 0.0504 µg/m³, and 0.0520 µg/m³, respectively. Meanwhile, the O₃ concentration in zone one (FAZ) tended to increase for every 1 unit (1 kg/day) of CH₄ or Other VOCs emissions increase (when the remaining four precursors were unchanged), were 0.0030 µg/m³ and 0.1689 µg/m³, respectively, which = was opposite to the change in emissions of CO, NO_x, or NMVOCs (when the remaining four precursors are unchanged), the O₃concentration decreased by 0.0010 µg/m³, 0.0214 µg/m³, and 0.0008 µg/m³, respectively. The O₃ concentration in zone seven (CPZ) tended to increase for every 1 unit (1 kg/day) of CH₄ or NMVOCs, or Other VOCs emissions increases (when the remaining four precursors were unchanged), were 0.0290 µg/m³, 0.0028 µg/m³, and 1.4978 µg/m³, respectively. This was opposite to the change in emissions of CO or NO_x (when the remaining four precursors were unchanged), the O₃ concentration decreased by 0.0143 µg/m³ and 0.0246 µg/m³, respectively.

Thus, the precursors CH₄, NO_x, NMVOCs, and Other VOCs all contributed to the increase in O₃concentrations in the PRZ, LXZ, TBZ, and CZ zones. Meanwhile, CH₄ and Other VOCs reflect the impact of increasing O₃ concentrations in only two areas, FAZ and CPZ. However, CO is the main factor reflecting the level of impact on reducing O₃ concentrations in the Mekong Delta, depending on certain geographical areas.

Based on the results of univariate analysis of each type of precursor emission, the correlation coefficient (R) of E_CH4, E_CO, and E_NOx was highest in zone two (PRZ) with explanatory levels of approximately 1.06%, 1.13%, and 1.20%, respectively, while the R of E_NMVOCsand E_Other-VOCs was highest in zone seven (CPZ) with explanatory levels of approximately 1.10% and 0.28%, respectively. The results of multivariate regression calculated at p-value showed that: (1) zone one (FAZ) where E_CH4 and E _Other-VOCs were statistically significant (p-value < 0.05); (2) zone two (PRZ), all E_CH4, E_CO, E_NOx, E_NMVOCs, and E_Other-VOCs were statistically significant; (3) zone 3 (LXZ), where E_CH4, E_CO, and E_NOx were statistically significant; (4) zone five (TBZ), where E_CH4, E_CO, E_NOx, and E_NMVOCswere statistically significant; (5) zone six CZ where E_CH4, E_CO, and E_NMVOCs were statistically significant; and (6) zone seven (CPZ), where only E_Other-VOCs were statistically significant. Thus, in the six zones in the MDR, emissions of precursors E_CH4, E_CO, E_NOx, and E_NMVOCs were independent factors that affect the O₃ concentration in these six zones. Meanwhile, Other-VOCsprecursor emissions had little or no influence on the O₃ concentrations in each zone of the MDR (Table 7)

Table 7. Synthesis of values in univariate linear regression analysis in 06 areas in the Mekong Delta of C_O3 = F(E_CH4), C_O3 = F(E_CO), C_O3 = F(E_NOx), C_O3 = F(E_NMVOCs) và C_O3 = F(E_Other-VOCs)

Factors	α	β	R	R²	p-value
Agro-ecological zone one, FAZ
E_CH4	44.05661	-0.00002	0.03943	0.00155	0.45267
E_CO	45.13400	-0.00001	0.04738	0.00224	0.36677
E_NOx	48.28175	-0.00072	0.06223	0.00387	0.23562
E_NMVOCs	46.85881	-0.00011	0.03381	0.00114	0.51962
E_Other-VOCs	28.80561	0.00859	0.01455	0.00021	0.78177
Agro-ecological zone two, PRZ
E_CH4	44.63183	-0.00016	0.10313	0.01064	0.04897
E_CO	45.21684	-0.00008	0.10614	0.01127	0.04270
E_NOx	45.59218	-0.00354	0.10963	0.01202	0.03629
E_NMVOCs	45.70085	-0.00045	0.06037	0.00364	0.24994
E_Other-VOCs	27.64205	0.29087	0.01741	0.00030	0.74023
Agro-ecological zone three, LXZ
E_CH4	42.91945	0.00027	0.04816	0.00232	0.35886
E_CO	42.77637	0.00012	0.04382	0.00192	0.40387
E_NOx	42.71862	0.00500	0.04275	0.00183	0.41552
E_NMVOCs	46.66667	-0.00070	0.02875	0.00083	0.58405
E_Other-VOCs	68.13774	-0.99878	0.02694	0.00073	0.60789
Agro-ecological zone five, TBZ
E_CH4	41.36059	0.00010	0.04663	0.00217	0.37439
E_CO	41.10480	0.00004	0.04162	0.00173	0.42793
E_NOx	41.07626	0.00162	0.03834	0.00147	0.46529
E_NMVOCs	39.56914	0.00044	0.03403	0.00116	0.51697
E_Other-VOCs	-4.65496	0.35598	0.03781	0.00143	0.47149
Agro-ecological zone six, CZ
E_CH4	47.21390	-0.00007	0.03172	0.00101	0.54578
E_CO	47.71331	-0.00004	0.03653	0.00133	0.48655
E_NOx	48.28819	-0.00199	0.04293	0.00184	0.41352
E_NMVOCs	43.43907	0.00039	0.02206	0.00049	0.67440
E_Other-VOCs	-3.41572	0.44490	0.03716	0.00138	0.47909
Agro-ecological zone seven, CPZ
E_CH4	44.13394	0.00016	0.05080	0.00258	0.33314
E_CO	43.74877	0.00007	0.04686	0.00220	0.37203
E_NOx	44.40019	0.00128	0.02032	0.00041	0.69882
E_NMVOCs	30.86259	0.00198	0.10508	0.01104	0.04483
E_Other-VOCs	-22.89717	0.55705	0.05261	0.00277	0.31619

Table 7. Synthesis of values in univariate linear regression analysis in 06 zones in the MDR of C_O3=F(E_CH4), C_O3=F(E_CO), C_O3=F(E_NOx), C_O3=F(E_NMVOCs) and C_O3=F(E_Other-VOCs)

3.4 Synthetic impacts of precursor emission and meteorological factors

A multivariable experimental linear regression model was built to evaluate the overall relationship between O₃ concentration (C_O3, y_3,j) and meteorological factors, including temperature value x₁ (T, ^oK), relative humidity x₂ (RH, %), surface pressure x₃ (P, Pa), and the types of CH₄ precursor emissions were E_CH4, x₄; CO was E_CO, x₅; NO_x was E_NOx, x₆; NMVOCs were E_NMVOCs, x₇; and Other VOCs were _EOther-VOCs, x₈. This result is shown in Table 8, along with (32) – (37).

Table 8. Multivariable correlation equation C_O3 = F (T, RH, P, E_CH4, E_CO_,E_NOx, E_NMVOCs , E_Other-VOCs)

Table 8 shows that the common correlation coefficients at six partitioned zones were R₁=0.47549, R₂=0.61360, R₃=0.53909, R₄=0.46066, R₅=0.47874 and R₆=0.42218, respectively. For zone one (FAZ), the factors of RH, P, and the emissions of E_NOx, E_NMVOCs and _EOther-VOCs affect the distribution of O₃ concentration in the zone; for zone two (PRZ), these are P, emissions of E_CH4, E_CO, E_NOx, E_NMVOCs and _EOther-VOCs. For zone three (LXZ), P, E_CH4, E_CO, E_NOx, and E_NMVOCs emissions; (4) for zone five (TBZ), P, emissions E_CH4, E_CO, and E_NOx; for zone six (CZ), P, emissions E_CH4, E_CO, E_NOx, and E_NMVOCs. Finally, in zone 7 (CPZ), the RH and P were the main independent factors with a major predictive significance for the individual O₃ concentration value for the six zones partitioned in MDR.

3.5 Uncertainty analysis

These limitations could lead to uncertainty in the research results and create errors in the simulation evaluation of ground-level O₃ concentration distribution. This could be explained in detail, first, by the limitation in the ground-level O₃ concentration monitoring data and the field data measured at meteorological observation stations in the MDR. For ground-level O₃ concentration measurement data in the entire MDR, there was no monitoring station to measure the ground-level O₃ concentration (it only monitored some basic pollution indicators, such as TSP, SO₂, NO_x, CO, and PM₁₀). The dataset in this study was collected from monitoring stations in the vicinity of Binh Duong Province, which was also an area within simulation domain D02 covering the entire study area. The ground-level O₃ concentration measurement dataset was created by the observation method, which was performed manually, and measured only four times at 9:00, 11:00, 13:00, and 15:00 on an important day unique monitoring at each station from January, 2018, to December, 2018.

Second, the contribution of the emission of two main precursors of NO_xand NMVOCs in each group of industries/activities was different among zones in the Mekong Delta, especially in zones one (FAZ), two (PRZ), five (TBZ), six (CZ), and seven (CPZ), with a of 520–1,530 thousand hectares compared to zone three (LXZ), and zone four (HMZ) with an area of only 95–225 thousand hectares. Significant differences in socio-economic activities, management levels of each locality, and the difference in soil problems by geographical zone (between freshwater alluvial soils and acid and saline soils) led to an assessment of six zones (not considering the four HMZ zones), which may not fully reflect the level of detail according to the administrative management level in each zone. Moreover, the 2018 emission inventory dataset built for the entire region (range of calculation D02) had a low resolution and a relatively large grid size (approximately 9.5 km) and natural emissions from forest fires in zones five (TBZ) and seven (CPZ) were not considered due to lack of detailed inventory data during the simulation.

Third, the correlations were limited to evaluation in the form of a multivariable linear function between O₃concentration and meteorological factors; the main precursor emissions were NO_x and NMVOCs. In this study, correlation of some forms of VOC precursor components according to the chemical mechanism of the CMAQ model was not performed. Moreover, the three selected meteorological factors, including temperature, relative air humidity, and surface pressure, cannot fully account for the impact of meteorology on the formation of O₃. As a result, the correlation level is not high enough (R only reaches a maximum of 0.61 in the two LXZ areas).

Fourth, in the MLR multivariable linear function models between total ground-level O₃ concentration and meteorological factors combined with emission precursors, the interaction between variables (combination of quadratic or ternary variables) increased the confidence level of the analytical model.

The seasonal variation of ground-level O₃ was noted in many regions of the world (Maji et al., 2019) (Tang et al., 2013), (Simon et al., 2012) in MDR, the change in ground-level O₃ concentration is quite complicated according to the season; in the dry season, the O₃ concentration is generally higher than that in the wet season with O₃ values. In the month with highest concentration of the dry season, February, the 1-hour average concentration ranged from 17.2 µg/m³ to 102.1 µg/m³, which was still significantly lower than the allowable limit of QCVN 05:2013/BTNMT (hourly average with 200 µg/m³), but different effects on crops should be considered. During the wet season, the highest O₃ value in the transitional period at the end of the season (October) ranged from 8.6 µg/m³ to 117.4 µg/m³. Considering the monthly average, the O₃ concentration was high (> 60 µg/m³), occurring in February, June, October, and December. In January, March–May, and July–September, the concentration level was much lower than in August (wet season), with the lowest concentration in the range of 16.7–25.1 µg/m³. October (end of the wet season) had the highest monthly average concentration, ranging from 55.4 µg/m³ to 84.5 µg/m³. The spatial distribution trend of terrestrial O₃ concentration shows that there is a clear and gradual shift from the inner delta to the eastern coastal areas in zone six (CZ), Cape Ca Mau in zone seven (CPZ), and the western coastal areas of zone 1 (FAZ) in Kien Giang Province.

Figure 6. Emission distribution of two precursors NO_x and NMVOCs with major impacts on ozone pollution in the Mekong Delta Region.

The role of precursors in the distribution of ozone concentrations has been discussed in similar studies (Xue et al., 2014; Sharma et al., 2016; Han et al., 2019; Chen et al., 2021;Mousavinezhad et al., 2021; Mitchell et al., 2021). The modelling results showed that the two precursors of NO_x and NMVOCs were the main contributors to ground-level O₃ pollution. These two precursors originate from anthropogenic activities as well as natural biological sources, with the largest contribution coming from zone one (FAZ) with 91.5 thousand tons of NO_x/year and 455.2 thousand tons of NMVOCs/year, followed by zone two (PRZ), zone seven (CPZ), zone five (TBZ), zone six CZ, and zone three (LXZ); the lowest was in zone four (HMZ) (Fig. 6). In particular, the NMVOCs precursors in each zone had a significant impact and were much more sensitive than the NO_x precursor emissions with the NMVOCs/NO_x ratio in zone one (FAZ) of 4.97, zone two (PRZ) was 8.03 times, zone three (LXZ) was 7.84 times; zone four (HMZ) was 6.82 times, zone five (TBZ) was 6.55 times, zone six (CZ) was 5.50 times and zone seven (CPZ) was 5.55 times. The pollution contribution was predominantly from anthropogenic activities with three main groups of industries/activities in the following order: (1) industrial production activities, (2) activities from residential zones and people's livelihood, and (3) agricultural production activities such as rice and crop cultivation, using land, livestock, etc.

Meteorology is one of the factors that directly affect the distribution of ground – level ozone concentration (Camalier et al., 2007; Davis et al., 2011; Godowitch et al., 2015; Mousavinezhad et al., 2021). The results showed that relative humidity (RH) and surface pressure (P) were the two main factors contributing to the increase in ground-level O₃ concentration, specifically, the increase in ground-level O₃ concentration from 5.9–8.7 µg/m³/Pa with each pressure unit increase (when temperature and relative humidity did not change) respectively for zone three (LXZ), zone five (TBZ), zone two (PRZ), zone one (FAZ), zone seven (CPZ) and zone six (CZ); and from 0.5–0.9 µg/m³/ % with each unit of humidity increase (when surface temperature and pressure remain unchanged) for zones two (PRZ), zone six (CZ), zone one (FAZ), zone three (LXZ), zone seven (CPZ) and zone five (TBZ), respectively. Moreover, the correlations between the ground-level O₃ concentration values and NO_x and NMVOCs precursor emissions were statistically significant (p < 0.05), showing that they contributed to the increase in ground-level O₃ concentration in zones two (PRZ), three (LXZ), five (TBZ), and six (CZ), whereas this is the opposite in zones one (FAZ) and seven (CPZ). At the same time, when evaluating the correlations between the ground-level O₃ concentration and meteorological factors combined with precursor emissions, the correlation index (R) gives results that are at a satisfactory level with values fluctuating from 0.42 to 0.61 (R > 0.4).

The MDR has a strategic location in Vietnam, bordering the East Sea and the Southwest Sea, the exclusive economic zone on the east, west, and south. It has a very favourable environment for growing rice, an important food crop globally. Rapid industrialisation in the Mekong Delta has increased the trend of ground – level zone concentrations, leading to a decline in crop yields. The distribution of ozone concentration for a year was modelled to determine the dependence of ozone concentration on emission factors and meteorology. Using a combination of WRF/CMAQ and multiple linear regression models, the following results were obtained.

First, the ground-level O₃ concentration during the dry season was higher than that in the wet season; the month with the highest concentration reaches an average concentration of 1 h, ranging from 17.2 µg/m³ to 102.1 µg/m³. In the wet season, the O₃ value was highest at the end of October, reaching 8.6–117.4 µg/m³. Within the Mekong Delta, ground-level ozone concentrations ranged from 40.39 µg/m³ to 52.13 µg/m³.

Second, the Mekong Delta was divided into six agro-ecological zones corresponding to geographical features, agriculture, and water resources: the FAZ, PRZ, LXZ, TBZ, CZ, and CPZ. The two precursors of NO_x emissions and NMVOCs are major contributors to terrestrial O₃ pollution. The largest contribution of emissions comes from FAZ with 91.5 thousand tons of NO_x/year and 455.2 thousand tons of NMVOCs/year. Next, is zone two (PRZ), zone seven (CPZ), zone five (TBZ), zone six (CZ), and the lowest was zone four (mountainous).

Third, in each zone, the NMVOCs precursors have a significant impact and are much more sensitive than the NO_x precursor emissions with the NMVOCs/NOx ratio in the first zone (FAZ) of 4.97, respectively. times, zone 2 (PRZ) is 8.03 times, zone 3 (LXZ) is 7.84 times; zone 4 (HMZ) is 6.82 times, zone 5 (TBZ) is 6.55 times, zone 6 (CZ) is 5.50 times and zone 7 (CPZ) is 5.55 times. Anthropogenic activities were the main sources of pollution, from three main groups of industries/activities in the following order: (1) industrial production activities, (2) residential zones and people's livelihood activities, and (3) from agricultural production activities such as rice and crop cultivation, using land, livestock, etc.

Fourth, in the sixth zone, the emissions of precursors E_CH4, E_CO, E_NOx, and E_NMVOCs were independent factors mainly affecting the ground-level O₃ concentration in these six zones. Other-VOCs precursor emissions had little or no influence on the O₃ concentration values in each area of the MDR.

Fifth, humidity (RH) and surface pressure (P) were the two main meteorological factors that contributed to the increase in O₃ concentration, among the three meteorological factors considered in this study, the humidity (RH), surface pressure (P), and temperature (T).

Ethical Approval

The authors declare :

The manuscript is not submitted to more than one journal for simultaneous consideration.

The manuscript is original and not have been published elsewhere in any form or language (partially or in full), unless the new work concerns an expansion of previous work.

The manuscript is not split up into several parts to increase the quantity of submissions and submitted to various journals or to one journal over time (i.e. ‘salami-slicing/publishing’).

Results are presented clearly, honestly, and without fabrication, falsification or inappropriate data manipulation. We adhere to discipline-specific rules for acquiring, selecting and processing data.

We have provided all data and proper mentions of other works

Consent to Participate

I consent to participate publish my manuscript entitled “Ground-level ozone in Mekong delta region: precursors, meteorological factors, and regional transport “ to the Environmental Science and Pollution Research (ESPR).

Consent to Publish

I consent to publish my manuscript entitled “Ground-level ozone in Mekong delta region: precursors, meteorological factors, and regional transport “ to the Environmental Science and Pollution Research (ESPR).

Authors Contributions

Long Ta Bui: Conceptualization, Funding acquisition, Investigation, Project administration, Resources, Supervision, Methodology, Models, writing - original draft, writing - review \& editing.

Phong Hoang Nguyen: Data curation, Data analysis, Formal analysis, Validation,GIS

Funding

We acknowledge the support of time and facilities from Ho Chi Minh City University of Technology (HCMUT), VNU- HCM for this study.

Competing Interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Availability of data and materials

We declare that all data relating to this manuscript are truthful and we will gladly share it with any interested readers or at the request of the editor board.

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You are reading this latest preprint version

Ground-level ozone in Mekong delta region: precursors, meteorological factors, and regional transport

Status:

Version 1

Abstract

Figures

1 Introduction

2 Data And Method

2.1 Study area

2.2 Data

2.2.1 Meteorology

2.2.2 Observation of ground-level O₃

2.2.3 Emission inventory datasets

2.3 WRF/CMAQ

2.3.1 Nested domain setup in the coupled WRF/CMAQ

2.3. 2 WRF/CMAQ modelling performance evaluation

2.4 Multiple linear regression

2.5 Meteorological modelling

2.6 Implementation steps

3 Result

3.1 Spatio - temporal distributions of ground-level ozone concentration

3.2 Impacts of meteorological factors

3.3 Impacts of precursor emission factors

3.4 Synthetic impacts of precursor emission and meteorological factors

3.5 Uncertainty analysis

4 Discussion

5 Conclusion

Declarations

References

Supplementary Files

Status:

Version 1

The statistical indexes	Range of value	Benchmarks	References
NSE (Nash)	(-\(\infty\); +1]	0.40–0.65: (GSO, 2020)Pass 0.65–0.85: Good > 0.85: Excellent	(McCuen et al., 2006; Xiaohui Zhong et al., 2015; Knoben et al., 2019)
MB (Mean Bias)	(-\(\infty\); +\(\infty\))	-	(Morris et al., 2005; Eder et al. 2006; Emery et al., 2017; Nguyen et al., 2020)
RMSE (Root Mean Squared Error)	(-\(\infty\); +\(\infty\))	-	(Morris et al., 2005; Eder et al., 2006; Emery et al., 2017; Nguyen et al., 2020)
NMB (Normalised Mean Bias)	(-100%; +\(\infty\))	-30% < NMB < + 30%	(Morris et al., 2005; Eder et al., 2006; Emery et al., 2017; Nguyen et al., 2020),
NME (Normalised Mean Gross Error)	(0%; +\(\infty\))	< 50%	(Morris et al., 2005; Eder et al. 2006; Emery et al., 2017; Nguyen et al., 2020)
Correlation coefficient R	[-1; +1]	R < 0.40: Not pass 0.40 < R < 0.80: Pass 0.80 < R < 0.85: Good R > 0.85: Excellent	(Emery et al., 2017; Nguyen et al., 2020)

Ground-level ozone in Mekong delta region: precursors, meteorological factors, and regional transport

Status:

Version 1

Abstract

Figures

1 Introduction

2 Data And Method

2.1 Study area

2.2 Data

2.2.1 Meteorology

2.2.2 Observation of ground-level O3

2.2.3 Emission inventory datasets

2.3 WRF/CMAQ

2.3.1 Nested domain setup in the coupled WRF/CMAQ

2.3. 2 WRF/CMAQ modelling performance evaluation

2.4 Multiple linear regression

2.5 Meteorological modelling

2.6 Implementation steps

3 Result

3.1 Spatio - temporal distributions of ground-level ozone concentration

3.2 Impacts of meteorological factors

3.3 Impacts of precursor emission factors

3.4 Synthetic impacts of precursor emission and meteorological factors

3.5 Uncertainty analysis

4 Discussion

5 Conclusion

Declarations

References

Supplementary Files

Status:

Version 1

2.2.2 Observation of ground-level O₃