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A precise air pollution forecast is the basis for targeted pollution control and sustained improvements in air quality. It is desirable and crucial to select the most suitable model for air pollution forecasting (APF). To achieve this goal, this paper provides a comprehensive evaluation of performances of different models in simulating the most common air pollutants (e.g., PM2.5, NO2, SO2, and CO) in Guangzhou (23.13° N, 113.26° E), China. To simulate temporal variations of the above-mentioned air pollutant concentrations in Guangzhou in September and October 2020, we use a numerical forecasting model (i.e., the Weather Research and Forecasting model with Chemistry (WRF-Chem)) and two artificial intelligence models (i.e., the back propagation neural network (BPNN) model and the long short-term memory (LSTM) model). WRF-Chem is also used to simulate the meteorological elements (e.g., the 2 m temperature (T2), 2 m relative humidity (RH), and 10 m wind speed and direction (WS, WD)). In order to investigate the simulation accuracies of classical APF models, we simultaneously compare the simulations of the WRF-Chem, BPNN, and LSTM models to ground truth observations. Comparative assessment results show that WRF-Chem simulated air pollutant (i.e., PM2.5, NO2, SO2, and CO) concentrations have the best correlations with ground measurements (i.e., Pearson correlation coefficient R = 0.88, 0.73, 0.61, and 0.61, respectively). Furthermore, to evaluate model performance in terms of accuracy and stability, the normalized mean bias (NMB, %) and mean fractional bias (MFB, %) are adopted as the standard performance metrics (SPMs) proposed by Boylan et al. The comparison results indicate that when simulating PM2.5, WRF-Chem was more effective than the BPNN but less effective than the LSTM. While simulating concentrations of NO2, SO2, and CO, the WRF-Chem model performed better than the BPNN and LSTM models. With regards to WRF-Chem, the NMBs and MFBs for the PM2.5 simulations are, respectively, 6.49% and 0.02%, –11.96% and –0.031% for NO2, 7.93% and 0.019% for CO, and 5.04% and 0.012% for SO2. Our results suggest that WRF-Chem has superior performance and better accuracy than the NN-based prediction models, making it a promising and useful tool to accurately predict and forecast regional air pollutant concentrations on a city scale. [ABSTRACT FROM AUTHOR]

This study on the warm-sector heavy rainfall event in Guangzhou on 7 May 2017, examined the effects and mechanisms of incorporating 30 m high-resolution land surface data into its numerical simulation. The updated 1km numerical model, integrating 30 m high-resolution land surface data, successfully captured the initiation, back-building, and organized development of warm-sector convections in Huadu and Zengcheng District. The analysis revealed that the high spatial resolution of the surface data led to a reduced urban area footprint (urban −6.31%), increased vegetation cover (forest 11.63%, croplands 1%), and enhanced surface runoff (water 2.77%) compared with a model's default land cover (900 m). These changes mitigated the urban heat island (UHI) effect within the metropolitan area and decreased the surface sensible heat flux. This reduction contributed to a pronounced temperature gradient between Huadu Mountain and the urban area. Additionally, a stronger high-pressure recirculation and sea–land breezes facilitated the transport of warm and moist air from the sea inland, creating a humid corridor along the sea–land interface. The consistent influx of warm and moist air near the mountain front, where strong temperature gradients were present, forcibly triggered warm-sector convection, intensifying its organization. This study highlighted the critical role of high-resolution land surface data in the accurate numerical simulation of warm-sector heavy rainfall. [ABSTRACT FROM AUTHOR]

In this study, we investigate the seasonal and diurnal variations in cloud occurrence frequency, as well as cloud vertical structure (CVS) characteristics under different seasons and precipitation intensities over the Guangzhou region in South China, based on the analysis of millimeter-wave cloud radar (MMCR) and ground automatic weather station rainfall observations from May 2019 to August 2021. The results showed that the occurrence frequency of clouds exhibits a bimodal distribution throughout the year, with peaks in March to June and October, reaching its highest occurrence in May at approximately 80% and its lowest from December to February at around 40%. Additionally, there are distinct diurnal variations in occurrence frequency, with the lowest rates occurring around 0005 LST, rapidly increasing after 0006 LST, and peaking during the afternoon to evening hours. Cloud top height (CTH) shows bimodal distributions during the pre-flood and post-flood seasons. The most frequently occurring range of CTH during the pre-flood season is below 3 km, accounting for approximately 43%, while during the post-flood season, it ranges from 11 to 14 km, constituting about 37%. For precipitation clouds, CTH can extend beyond 12 km, with the radar reflectivity decreasing gradually with increasing height. The highest frequencies of radar echoes are observed below 2 km and between 4 and 7 km, exhibiting clear diurnal variations, with echoes mainly below 2 km and between 4 to 6 km during the early morning, intensifying and shifting to higher altitudes during the day and reaching their maximum below 4 km during the afternoon to nighttime hours, while both the frequency and intensity increase in the height range of 4 to 12 km. Vertical profiles of radar reflectivity and cloud ice/liquid water content (IWC/LWC) exhibit similar trends under different precipitation intensities. The main differences are observed below 4 km, where both radar reflectivity and IWC/LWC generally increase with increasing precipitation intensity. These findings contribute to a better understanding of cloud characteristics in the South China region, enhance the accuracy of model simulations, and provide a scientific basis for accurate forecasting and warning of meteorological disasters. [ABSTRACT FROM AUTHOR]

Near-surface PM2.5 concentrations have been greatly exacerbated by urban land expansion and dense urban traffic. This study aims to clarify the effects of multiple factors on near-surface PM2.5 concentrations from three perspectives of background climatic variables, urban morphology variables, and traffic-related emission intensity. First, two case areas covering multiple local blocks were selected to conduct mobile measurements under different climatic conditions. The observed meteorological parameters and PM2.5 concentration were obtained through GIS-based imaging. These interpolation results of air temperature and relative humidity reveal highly spatiotemporal diversity, which is greatly influenced by artificial heat emissions and spatial morphology characteristics in local areas. The PM2.5 concentration on measurement days also varies considerably from the lowest value of 44~56 μg/m3 in October to about 500 μg/m3 in December in Harbin winter and ranges between about 5 μg/m3 and 50 μg/m3 in Guangzhou summer. The correlation analysis reveals that both the climatic conditions and urban morphology characteristics are significantly correlated with local PM2.5 concentration. Especially for Guangzhou summer, the PM2.5 concentration was positively correlated with the street traffic emission source intensity with correlation coefficient reaching about 0.79. Multivariate nonlinear formulas were applied to fit the association between these factors and PM2.5 concentration with higher determined coefficients. And optimization strategies are thus suggested to improve the urban air quality in local-scale areas. This attribution analysis contributes to environmentally friendly urban construction. [ABSTRACT FROM AUTHOR]

Carbonaceous aerosols (CAs), including elemental carbon (EC) and organic carbon (OC), have become the dominant component in PM2.5 in many Chinese cities, and it is imperative to address their spatiotemporal variations and sources in order to continually improve air quality. In this study, the mass concentrations and light absorption properties of EC and OC in PM2.5 were investigated at diverse sites in Guangzhou, in the winter of 2020 and the autumn of 2021, using the DRI Model 2015 thermal–optical carbon analyzer. The results showed that total EC and organic matter (OM = OC × 1.8) could account for nearly 30% of the PM2.5 mass concentrations. Secondary production was the most important source for OC, with secondary OC (SOC) percentages in the OC as high as 72.8 ± 7.0% in autumn and 68.4 ± 13.1% in winter. Compared to those in 2015, OC and EC concentrations were reduced by 25.4% and 73.4% in 2021, highlighting the effectiveness of control measures in recent years. The absorption coefficient of brown carbon at 405 nm (babs,BrC,405) decreased by over 40%, and the mass absorption coefficient (MAC) at 405 nm of total carbon (TC) decreased by over 30%. EC and OC concentrations and the light absorption of black carbon (babs,BC,405) showed no significant diurnal differences in both autumn and winter mainly because the reduction in anthropogenic emissions at night was compensated by the lowering of the boundary layer. Differentially, babs,BrC,405 was significantly lower during daytime than at night in autumn, probably due to the daytime photobleaching effect. The sources of EC, OC, BC, and BrC were preliminarily diagnosed by their correlation with typical source markers. In autumn, babs,BrC,405 might be related to biomass burning and coal combustion, while babs,BC,405 were largely related to vehicle emissions and coal combustion. In winter, babs,BrC,405 was closely related to coal combustion. [ABSTRACT FROM AUTHOR]

X-band dual-polarization phased array radar (XPAR-D) possesses high resolution and plays a significant role in detecting meso- and micro-scale convective systems. However, the precipitation attenuation it endures necessitates an effective correction method. This study selected radar data from XPAR-D at the peak of Maofeng Mountain in Guangzhou during 16–17 May 2020 from three precipitation stages after quality control. Attenuation coefficients were calculated for different precipitation types through scattering simulations of raindrop size distribution (RSD) data. Next, an attenuation correction algorithm (MZH-KDP method) was proposed for the radar reflectivity factor (ZH) according to different raindrop types and compared to the ZH-KDP method currently in use. The results indicate that the attenuation amount of XPAR-D echoes depends on the attenuation path and echo intensity. When the attenuation path is shorter and the echo intensity is weaker, the amount of attenuation and correction is smaller. Difficulties arise when there are noticeable deviations, which are challenging to resolve using attenuation correction methods. Longer attenuation paths and stronger echoes highlight the advantages of the MZH-KDP method, while the ZH-KDP method tends to overcorrect the bias. The MZH-KDP method outperforms the ZH-KDP method for different precipitation types. The superior correction capability of the MZH-KDP method provides a significant advantage in improving the performance of XPAR-D for the detection of extreme weather. [ABSTRACT FROM AUTHOR]

Urban travel is a major component of urban transport, and the total carbon emissions of urban transport can be effectively reduced if the carbon emissions of residents' travel can be reasonably controlled. Based on the questionnaire data of many different types of residential areas in Guangzhou, the daily travel behaviour of residents was analysed and their travel carbon emissions were calculated. The differences in travel characteristics and travel carbon emissions of residents in different types of residential areas were compared. The influencing factors of residents' travel carbon emissions were divided into three categories: individual socio-economic factors, built environment, and residents' travel behaviour. On this basis, a structural equation model is established to study the impact mechanism of residents' travel carbon emissions. The path relationship between endogenous variables and endogenous variables, as well as the path relationship between exogenous variables and endogenous variables were investigated. The interactions between various influencing factors of residents' travel carbon emissions were examined. The results show that: (1) there are significant differences in residents' travel carbon emissions in different types of municipalities. Residents' travel carbon emissions in Guangzhou are closer to the 60/20 distribution; (2) compared with individual socio-economic factors, the impact of the built environment on travel carbon emissions is more obvious; (3) travel distance and travel mode are the factors that directly affect residents' travel carbon emissions. Proposals have been made to reduce the carbon emissions of residents travelling. [ABSTRACT FROM AUTHOR]

Atmospheric levels of atomic mercury pollution were measured using spectroscopic techniques in the city of Guangzhou, Guangdong Province, China. Assessments were mainly performed at ground level using a portable (vehicle mounted or hand carried) Zeeman modulation correlation spectrometer (Lumex RA-915M), and the results are given in easily comprehensible diagrams. Measurements were made with continuous recording in car traverses along major roads which cross the city, but also at selected spots, such as at a university campus with laboratory buildings. Further, pollution levels at different locations were recorded when walking through a major and a small hospital. While concentrations in the city in the range 3–10 ng/m3 were typical, and strongly dependent on the traffic situation, very high concentrations (up to 1300 ng/m3) were found at certain indoor hospital locations, again drawing attention to the fact that high mercury levels due to inadequate handling routines can remain undetected but could readily be eliminated by adequate measurements and subsequent sanitation. [ABSTRACT FROM AUTHOR]

The global climate has changed significantly, characterized by the warming of the surface air temperature, which seriously affects public health. We examined the trends of extreme temperatures, heat waves and cold spells in a subtropical city of Guangzhou, China, during 1951-2015. Specifically, the relationship between ENSO (El Niño-Southern Oscillation) events and heat waves/cold spells was discussed. The results of linear regression showed the annual mean temperature and extreme warm days increased (0.14 °C/decade and 6.26 days/decade) while extreme cold days decreased significantly (1.77 days/decade). Heat waves were more frequent, longer lasting and had stronger intensity over the past 65 years. In addition, the frequency, duration and intensity of heat waves were correlated with annual Atlantic Multi-decadal Oscillation (AMO) and Indian Ocean Basin-wide Warming (IOBW), while there were no significant differences in the characteristics of heat waves among an El Niño year, a La Niña year and a Neutral year. In contrast, neither significant trend nor association with ENSO events was observed for cold spells. In conclusion, our study indicated an obvious increasing trend for all aspects of heat waves in Guangzhou, China. [ABSTRACT FROM AUTHOR]

Increases in ground-level ozone (O3) have been observed during the COVID-19 lockdown in many places around the world, primarily due to the uncoordinated emission reductions of O3 precursors. In Guangzhou, the capital of Guangdong province in South China, O3 distinctively decreased during the lockdown. Such a phenomenon was attributed to meteorological variations and weakening of local O3 formation, as indicated by chemical transport models. However, the emission-based modellings were not fully validated by observations, especially for volatile organic compounds (VOCs). In this study, we analyzed the changes of O3 and its precursors, including VOCs, from the pre-lockdown (Pre-LD) to lockdown period (LD) spanning 1 week in Guangzhou. An observation-based box model was applied to understand the evolution of in-situ photochemistry. Indeed, the ambient concentrations of O3 precursors decreased significantly in the LD. A reduction of 20.7% was identified for the total mixing ratios of VOCs, and the transportation-related species experienced the biggest declines. However, the reduction of O3 precursors would not lead to a decrease of in-situ O3 production if the meteorology did not change between the Pre-LD and LD periods. Sensitivity tests indicated that O3 formation was limited by VOCs in both periods. The lower temperature and photolysis frequencies in the LD reversed the increase of O3 that would be caused by the emission reductions otherwise. This study reiterates the fact that O3 abatement requires coordinated control strategies, even if the emissions of O3 precursors can be significantly reduced in the short term. [ABSTRACT FROM AUTHOR]

The capability to estimate monsoon rainfall is investigated by using S-band polarimetric radar (S-POL) and two-dimensional Video Disdrometer (2DVD) during 2017–2018 in South China. Based on 2 years of 2DVD raindrop size distribution (DSD) observations of monsoon precipitation systems, four different quantitative precipitation estimation (QPE) algorithms were obtained, including R(ZH), R(ZH, ZDR), R(KDP), and R(KDP, ZDR). In order to clearly demarcate the optimal ranges of the four QPE algorithms by considering the impact of the monsoon precipitation system of South China, the optimal ranges of the four QPE algorithms were integrated together according to the characteristics of different QPE algorithms in the reflectivity-differential reflectivity (ZH-ZDR) space distribution by reference to 8 monsoon rainfall events from 2016 to 2020 observed in Guangzhou and Yangjiang S-POL. Then, an optimal algorithm was proposed for the quantitative estimation of monsoon precipitation in South China (2DVD-SCM) using S-POL. The 2DVD-SCM was tested by comparing it with a traditional radar QPE algorithm PPS (WSR-88D Precipitation Processing System); a classical QPE algorithm CSU-HIDRO (Colorado State University-Hydrometeor Identification Rainfall Optimization) for the polarimetric radar; a piecewise fitting algorithm LPA-PFM (Piecewise Fitting Method) based on laser raindrop spectrum. The rainfall event one-by-one test results show that the 2DVD-SCM algorithm performs obviously better than the other three algorithms in most of the rainfall events. The hourly accumulated rainfalls estimated by the 2DVD-SCM algorithm are agreed well with rain gauge observations. The normalized errors (NE) and the root mean square errors (RMSE) values of 2DVD-SCM are remarkably less than the other three algorithms, and the correlation coefficient (CC) values are higher. The results of the classified rain rate test show that the NE and RMSE values of the 2DVD-SCM algorithm are the lowest in all classified rain rates. The overall evaluation results show that the 2DVD-SCM algorithm performs obviously better than the existing three algorithms and have the potential to apply in S-band polarimetric radar monsoon rainfall estimation operational system in South China. [ABSTRACT FROM AUTHOR]

The Model Output Statistics (MOS) model is a dynamic statistical weather forecast model based on multiple linear regression technology. It is greatly affected by the selection of parameters and predictors, especially when the weather changes drastically, or extreme weather occurs. We improved the traditional MOS model with the machine learning method to enhance the capabilities of self-learning and generalization. Simultaneously, multi-source meteorological data were used as the input to the model to improve the data quality. In the experiment, we selected the four areas of Nanjing, Beijing, Chengdu, and Guangzhou for verification, with the numerical weather prediction (NWP) products and observation data from automatic weather stations (AWSs) used to predict the temperature and wind speed in the next 24 h. From the experiment, it can be seen that the accuracy of the prediction values and speed of the method were improved by the ML-MOS. Finally, we compared the ML-MOS model with neural networks and support vector machine (SVM), the results show that the prediction result of the ML-MOS model is better than that of the above two models. [ABSTRACT FROM AUTHOR]

To accurately apportion the sources of aerosols, a combined method of positive matrix factorization (PMF) and the Bayesian mixing model was applied in this study. The PMF model was conducted to identify the sources of PM2.5 in Guangzhou. The secondary inorganic aerosol source was one of the seven main sources in Guangzhou. Based on stable isotopes of oxygen and nitrogen (δ15N-NO3− and δ18O-NO3−), the Bayesian mixing model was performed to apportion the source of NO3− to coal combustion, traffic emission and biogenic source. Then the secondary aerosol source was subdivided into three sources according to the discrepancy in source apportionment of NO3− between PMF and Bayesian mixing model results. After secondary aerosol assignment, the six main sources of PM2.5 were traffic emission (30.6%), biomass burning (23.1%), coal combustion (17.7%), ship emission (14.0%), biomass boiler (9.9%) and industrial emission (4.7%). To assess the source apportionment results, fossil/non-fossil source contributions to organic carbon (OC) and element carbon (EC) inferred from 14C measurements were compared with the corresponding results in the PMF model. The results showed that source distributions of EC matched well between those two methods, indicating that the PMF model captured the primary sources well. Probably because of the lack of organic molecular markers to identify the biogenic source of OC, the non-fossil source contribution to OC in PMF results was obviously lower than 14C results. Thus, an indicative organic molecular tracer should be used to identify the biogenic source when accurately apportioning the sources of aerosols, especially in the region with high plant coverage or intense biomass burning. [ABSTRACT FROM AUTHOR]

Ozone (O3) pollution has become an increasing concern in China since elevated surface O3 concentrations were observed in recent years. In this study, five provincial cities (Beijing, Shanghai, Guangzhou, Xi'an, and Hefei) located in different regions of China were selected to study the spatiotemporal variations and affecting factors of O3 concentrations during 2014–2015. Beijing, Shanghai, and Guangzhou had suffered more severe O3 pollution, yet Beijing had the highest number of days that exceeded the Chinese MDA8 (maximum daily 8 h average) standard of 160 µg m−3. MDA8 O3 exhibited different seasonal patterns among the five cities. In Beijing and Xi'an, MDA8 O3 showed the highest in summer and lowest in winter. Guangzhou also had the highest O3 concentration in summer, but had similar levels in other three seasons. The O3 levels were similarly high in Shanghai during spring, summer, and autumn, while in Hefei, O3 concentration peaked in autumn. No significant difference between weekend and weekday O3 levels was observed in all the five cities. The diurnal cycle reached a maximum in the afternoon and a minimum in the early morning, which was consistent in the five cities. Correlation analyses showed that the associations between O3 and the other five criteria air pollutants, as well as meteorological parameters, were substantially different among the five cities. Air mass cluster analyses during episodic days revealed that the short-distance transport of O3 and its precursors had a greater impact for high O3 pollution in the five cities. Overall, our results demonstrate that O3 pollution exhibited great divergence among different regions and thus region-oriented control measures are suggested to reduce O3 pollution in China. [ABSTRACT FROM AUTHOR]

Isoprene has a potentially large effect on ozone (O3) formation in the subtropical, highly polluted city of Guangzhou. Online measurements of isoprene in Guangzhou city are scarce; thus, isoprene levels were monitored for one year at the Guangzhou Panyu Atmospheric Composition Station (GPACS), a suburban site in Guangzhou, using an online gas chromatography-flame ionization detector (GC–FID) system to investigate the characterization and reactivity of isoprene and its effect on the O3 peak profile in different seasons. The results showed that the daily average mixing ratios of isoprene at GPACS were 0.40, 2.20, 1.40, and 0.13 mixing ratio by volume (ppbv) in spring, summer, autumn, and winter, respectively. These values were considerably higher than the mixing ratios of isoprene in the numerous other subtropical and temperate cities around the world. Furthermore, isoprene ranked first with regard to O3 formation potential (OFP) and propylene-equivalent mixing ratio among 56 measured non–methane hydrocarbons (NMHCs). The ratios of isoprene to cis-2-butene, an exhaust tracer, were determined to estimate the fractions of biogenic and anthropogenic emissions. The results revealed a much greater contribution from biogenic than anthropogenic factors during the daytime in all four seasons. In addition, night-time isoprene emissions were mostly associated with vehicles in winter, and the residual isoprene that remained after photochemical loss during the daytime also persisted into the night. The high levels of isoprene in summer and autumn may cause the strong and broad peaks of the O3 profile because of its association with the most favorable meteorological conditions (e.g., high temperature and intense solar radiation) and the highest OH mixing ratio, which could affect human health by exposing people to a high O3 mixing ratio for prolonged periods. The lower mixing ratios of isoprene resulted in a weak and sharp peak in the O3 profile in both spring and winter. The high level of isoprene in the subtropical zone could accentuate its large impact on atmospheric oxidant capacity and air quality in Guangzhou city. [ABSTRACT FROM AUTHOR]