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The Aethalometer is a widely used instrument for black carbon (BC) mass concentration and light absorption coefficient (babs) measurements around the world. However, field intercomparison of the two popular models, dual-spot (AE33) and single-spot (AE31) Aethalometers, remains limited; in addition, the difference in secondary brown carbon (BrCsec) light absorption estimation between the two models is largely unknown. We performed full-year collocated AE33 and AE31 measurements in a megacity in southern China – Guangzhou. The babs values agree well between the two Aethalometers (R2 > 0.95), with AE33 / AE31 slopes ranging from 0.87 to 1.04 for seven wavelengths. AE33 consistently exhibits lower limits of detection (LODs) than AE31 for time resolutions of 2 to 60 min. The AE33 / AE31 slope for equivalent BC (eBC) was 1.2, implying the need for site-specific post-correction. The absorption Ångström exponent (AAE) obtained from different approaches does not agree very well between the two models, with the biggest discrepancy found in AAE880/950. The estimated BrCsec light absorption at 370 nm (babs370_BrCsec) was calculated using the minimum- R -squared (MRS) method for both Aethalometers. The babs370_BrCsec comparison yields a slope of 0.78 and an R2 of 0.72 between the two models, implying a non-negligible inter-instrument difference. This study highlights the high consistency in babs but less so in AAE between AE31 and AE33 and reveals site-specific correction for eBC estimation and non-negligible difference in BrCsec estimation. The results are valuable for data continuity in long-term Aethalometer measurements when transitioning from the older (AE31) to the newer (AE33) model, as anticipated in permanent global-climate and air-quality stations. [ABSTRACT FROM AUTHOR]

Building function type is an important parameter for urban planning and disaster management. However, existing identification methods do not always correctly recognize all building functions because of missing point of interest (POI) data in private areas. In this study, we proposed a hierarchical data-mining model to identify building function types using accessible auxiliary data, which was then applied to a case study. Residential building property was assessed to address missing residential POIs. The building functions were assigned to one of five different types, or a mixed-function type. Standard deviation and mean values extracted from remotely sensed images, distances to major roads, and building shape parameters were used to infer the function types of buildings without assigned function types. The proposed model was able to identify 65% of buildings not previously assigned as residential through the POI, with an overall accuracy of 87%. In addition, all buildings were successfully assigned a function type of residential, commercial, office, warehouse, public service, or mixed-function, with an overall accuracy of 85% for unclassified buildings. Our results demonstrated that missing POI data in private areas could be addressed by integration with multisource data using a simple method. [ABSTRACT FROM AUTHOR]

Black carbon (BC) aerosols have been widely recognized as a vital climate forcer in the atmosphere. Amplification of light absorption can occur due to coatings on BC during atmospheric aging, an effect that remains uncertain in accessing the radiative forcing of BC. Existing studies on the absorption enhancement factor (Eabs) have poor coverage on both seasonal and diurnal scales. In this study, we applied a recently developed minimum R squared (MRS) method, which can cover both seasonal and diurnal scales, for Eabs quantification. Using field measurement data in Guangzhou, the aims of this study are to explore (1) the temporal dynamics of BC optical properties at seasonal (wet season, 31 July–10 September; dry season, 15 November 2017–15 January 2018) and diel scales (1 h time resolution) in the typical urban environment and (2) the influencing factors on Eabs temporal variability. Mass absorption efficiency at 520 nm by primary aerosols (MAE p520) determined by the MRS method exhibited a strong seasonality (8.6 m 2 g -1 in the wet season and 16.8 m 2 g -1 in the dry season). Eabs520 was higher in the wet season (1.51±0.50) and lower in the dry season (1.29±0.28). Absorption Ångström exponent (AAE 470–660) in the dry season (1.46±0.12) was higher than that in the wet season (1.37±0.10). Collective evidence showed that the active biomass burning (BB) in the dry season effectively altered the optical properties of BC, leading to elevated MAE, MAE p and AAE in the dry season compared to those in the wet season. Diurnal Eabs520 was positively correlated with AAE 470–660 (R2=0.71) and negatively correlated with the AE33 aerosol loading compensation parameter (k) (R2=0.74) in the wet season, but these correlations were significantly weaker in the dry season, which may be related to the impact of BB. This result suggests that during the wet season, the lensing effect was more likely dominating the AAE diurnal variability rather than the contribution from brown carbon (BrC). Secondary processing can affect Eabs diurnal dynamics. The Eabs520 exhibited a clear dependency on the ratio of secondary organic carbon to organic carbon (SOC/OC), confirming the contribution of secondary organic aerosols to Eabs ; Eabs520 correlated well with nitrate and showed a clear dependence on temperature. This new finding implies that gas–particle partitioning of semivolatile compounds may potentially play an important role in steering the diurnal fluctuation of Eabs520. In the dry season, the diurnal variability in Eabs520 was associated with photochemical aging as evidenced by the good correlation (R2= 0.69) between oxidant concentrations (Ox=O3+NO2) and Eabs520. [ABSTRACT FROM AUTHOR]

Aberrant expression of microRNAs (miRNAs) has been implicated in human cancer, including colorectal cancer (CRC). Such dysregulated miRNAs may have potential as diagnostic markers or therapeutic targets. However, the nature of an association between these miRNAs and clinical stages of CRC is still not clear. To this end, we performed a miRNA profiling of 1547 distinct human miRNAs using 31 samples of tumor and paired normal mucosa obtained from 31 CRC patients. Based on statistical analyses of profiling data, we identified 569 miRNAs that were significantly dysregulated in CRC relative to normal tissues (P<0.05). Among the 569 dysregulated miRNAs, downregulation of 17 was associated with stages II, III, and IV colon and rectal cancers (separate or combined), according to our criteria. We also assessed the potential of these dysregulated miRNAs as diagnostic biomarkers for CRC patients who were without metastasis, and the value of the dysregulated miRNAs for predicting metastasis, lymph node and distant. Their distinct expression patterns in colon and rectal cancers were also examined. Although our findings cannot be immediately applied toward clinical diagnosis, our new study model for determining and assessing the biomarker potential of dysregulated miRNAs should be useful in further research in detection of human CRC. [ABSTRACT FROM AUTHOR]

This study explored the model of urban impervious surface (IS) density, land surface temperature (LST), and comprehensive ecological evaluation index (CEEI) from urban centers to suburbs. The interrelationships between these parameters in Guangzhou from 1987 to 2019 were analyzed using time-series Landsat-5 TM (Thematic Mapper), Landsat-8 OLI (Operational Land Imager), and TIRS (Thermal Infrared Sensor) images. The urban IS densities were calculated in concentric rings using time-series IS fractions, which were used to construct an inverse S-shaped urban IS density function to depict changes in urban form and the spatio-temporal dynamics of urban expansion from the urban center to the suburbs. The results indicated that Guangzhou experienced expansive urban growth, with the patterns of urban spatial structure changing from a single-center to a multi-center structure over the past 32 years. Next, the normalized LST and CEEI in each concentric ring were calculated, and their variation trends from the urban center to the suburbs were modeled using linear and nonlinear functions, respectively. The results showed that the normalized LST had a gradual decreasing trend from the urban center to the suburbs, while the CEEI showed a significant increasing trend. During the 32-year rapid urban development, the normalized LST difference between the urban center and suburbs increased gradually with time, and the CEEI significantly decreased. This indicated that rapid urbanization significantly expanded the impervious surface areas in Guangzhou, leading to an increase in the LST difference between urban centers and suburbs and a deterioration in ecological quality. Finally, the potential interrelationships among urban IS density, normalized LST, and CEEI were also explored using different models. This study revealed that rapid urbanization has produced geographical convergence between several ISs, which may increase the risk of the urban heat island effect and degradation of ecological quality. [ABSTRACT FROM AUTHOR]

Economic development, population growth, industrialization, and urbanization dramatically increase urban water quality deterioration, and thereby endanger human life and health. However, there are not many efficient methods and techniques to monitor urban black and odorous water (BOW) pollution. Our research aims at identifying primary indicators of urban BOW through their spectral characteristics and differentiation. This research combined ground in-situ water quality data with ground hyperspectral data collected from main urban BOWs in Guangzhou, China, and integrated factorial data mining and machine learning techniques to investigate how to monitor urban BOW. Eight key water quality parameters at 52 sample sites were used to retrieve three latent dimensions of urban BOW quality by factorial data mining. The synchronically measured hyperspectral bands along with the band combinations were examined by the machine learning technique, Lasso regression, to identify the most correlated bands and band combinations, over which three multiple regression models were fitted against three latent water quality indicators to determine which spectral bands were highly sensitive to three dimensions of urban BOW pollution. The findings revealed that the many sensitive bands were concentrated in higher hyperspectral band ranges, which supported the unique contribution of hyperspectral data for monitoring water quality. In addition, this integrated data mining and machine learning approach overcame the limitations of conventional band selection, which focus on a limited number of band ratios, band differences, and reflectance bands in the lower range of infrared region. The outcome also indicated that the integration of dimensionality reduction with feature selection shows good potential for monitoring urban BOW. This new analysis framework can be used in urban BOW monitoring and provides scientific data for policymakers to monitor it. Image 1 • Hyperspectral bands provide unique info concerning severity of black-odorous water. • Factorial data mining synthesizes water quality parameters into reduced dimensions. • Reduced dimensions establish correlation between water quality and spectral data. • Machine learning based feature extraction selects critical spectral bands. • Reduced dimensions and critical spectral bands reveal condition of water quality. [ABSTRACT FROM AUTHOR]

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Background: The first local outbreak of Delta Variant B.1.617.2 COVID-19 of China occurred in Guangzhou city, south China, in May 2021. This study analyzed the transmission chains and local cluster characteristics of this outbreak, intended to provide information support for the development and adjustment of local prevention and control strategies. Methods: The transmission chains and local cluster characteristics of 161 local cases in the outbreak were described and analyzed. Incubation period, serial interval and generation time were calculated using the exact time of exposure and symptom onset date of the cases. The daily number of reported cases and the estimated generation time were used to estimate the effective reproduction number (Rt). Results: We identified 7 superspreading events who had more than 5 next generation cases and their infected cases infected 70.81%(114/161) of all the cases transmission. Dining and family exposure were the main transmission routes in the outbreak, with 29.19% exposed through dining and 32.30% exposed through family places. Through further analysis of the outbreak, the estimated mean incubation period was 4.22 (95%CI: 3.66–4.94) days, the estimated mean generation time was 2.60 (95%CI: 1.96–3.11) days, and the estimated Rt was 3.29 (95%CI: 2.25–5.07). Conclusions: Classification and dynamically adjusted prevention and control measures had been carried out according to analysis of transmission chains and epidemical risk levels, including promoting nucleic acid screening at different regions and different risk levels, dividing closed-off area, controlled area according to the risk of infection, raising the requirements of leaving Guangzhou. By the above control measures, Guangzhou effectively control the outbreak within 28 days without implementing a large-scale lockdown policy. [ABSTRACT FROM AUTHOR]

Long-term air pollution data are essential for formulating air quality management policies and assessing their corresponding impacts on public health. However, missing data are inevitably encountered during air pollution observations at different sites. This study proposed a machine learning approach that utilizes data from neighboring sites to reconstruct missing data. Hourly observation data from three neighboring sites in the Pearl River Delta (PRD) region in South China, were used for data retrieval, including the NC site (2006–2015), JXL site and PYZX site (2014–2022). The overlapped data (2014.05–2015.12) were used to train and evaluate the machine learning models. The performance of 11 algorithms (CatBoost, XGBoost, LightGBM, LightGBMXT, LightGBMLarge, RandomForestMSE, ExtraTreeMSE, NeuralNetTorch, NeuralNetFastAI, KNeighborsDist, and KNeighborsUnif) for the retrieval of major air pollutants, including O 3 , NO 2 , PM 2.5 , PM 10 and SO 2 was benchmarked by a set of evaluation metrics. CatBoost showed the best performance; thus, it was adopted for air pollutant data reconstruction in NC (2016–2022) and PYZX (2008–2014). Long-term data (2006–2022) at the NC were obtained by combining the observation and retrieval data. In the past 15 years, the O 3 concentration of NC has increased by 72% at a rate of 0.83 ppb yr−1 (3.2% yr−1). On the contrary, substantial reductions were observed for NO 2 (61%), PM 2.5 (51%) and PM 10 (42%) at the NC site, with the rates of −1.27 ppb yr−1 (−5.9% yr−1), −1.96 μg m−3 yr−1 (−5.8% yr−1) and −2.32 μg m−3 yr−1 (−5.2% yr−1), respectively. SO 2 exhibits the most pronounced reduction (79%) among all species, with two distinct rates of −4.10 ppb yr−1 (−27.4% yr−1) and −0.40 ppb yr−1 (−6.2% yr−1), for 2008–2012 and 2012–2022, respectively. This study demonstrates the feasibility of machine learning in filling the data gap of air pollution monitoring network and highlights the importance of continuous long-term air pollution data in reviewing air quality management policies. [Display omitted] • A machine learning approach is proposed to reconstruct missing air quality data. • 11 algorithms were compared and CatBoost exhibited the best results. • Missing data reconstruction was demonstrated using data in Guangzhou. [ABSTRACT FROM AUTHOR]

Abstract: Near-surface atmospheric aerosol samples were collected at the sampling frequency of 2–3 d per week for one year from August 2006 to August 2007 at a low latitude station in Tianhe District, Guangzhou, Guangdong Province of southern China. The samples were analyzed for cosmogenic nuclide 7Be and persistent organic pollutants, i.e. organochlorine pesticides (OCPs), polychlorinated biphenyls (PCBs), and polybrominated diphenyl ethers (PBDEs). The annual average 7Be concentration was 2.59 mBq m−3, with the maximum occurred in May (8.45 mBq m−3) and minimum in late August and early September (0.07 mBq m−3). Winter and spring were the seasons in which the 7Be concentrations were high while summer and autumn were the lower 7Be seasons. Spring peaks in 7Be in the near-surface atmospheric aerosols may have associated with the “spring leak maximum” episode. The annual average ∑OCPs concentration was 345.6 pg m−3, ∑33PCBs 317.6 pg m−3, and ∑31PBDEs 609.0 pg m−3. The variation trends in the time-series of 7Be, OCPs, PCBs, and PBDEs in near-surface atmospheric aerosol showed both common features and differences. Significant correlations (R 2 = 0.957 and 0.811. respectively, p = 0.01) were observed between the monthly average 7Be concentrations and those of ∑PCBs and ∑PBDEs in summer, autumn, and early winter. The difference between the seasonal variation features of OCPs and PCBs (and PBDEs) could be attributed to the different source functions and physical–chemical properties which could control the behaviors of these compounds in air–aerosol partitions as well as atmospheric transport. [Copyright &y& Elsevier]

Improving the comprehensive utilization level of energy has been a core goal for the optimal design of the integrated energy system (IES), which requires an accurate assessment of energy efficiency. The widely used criterion of conventional energy efficiency derived from the first law of thermodynamics has gradually been criticized for its shortcomings of neglecting the quality of energy. As a concept considering both the quantity and quality of energy, exergy efficiency has been recognized as a more reasonable index in recent decades. Nevertheless, it is a complicated problem to calculate the exergy efficiency among multiple energy forms in an electricity-gas-heating-cooling coupled IES, limiting its application in IES planning. This paper hence proposes the method of calculating the exergy efficiency of an IES by using a black-box model, after defining the concept of energy quality coefficient (EQC) that values the quality of various energy forms involved. And then, a multi-objective planning model considering both exergy efficiency and economy is thus proposed for the joint planning of energy generators, storages and networks, wherein a five-tier energy hub is modeled to deal with the multi-energy couplings. To reduce the difficulty of optimizing, several convex relaxation measures are designed to transform the non-convex original problem into a convex one. The proposed framework is verified in a real-world study case located in Guangzhou, China. • A criterion for accessing comprehensive energy utilization level is proposed. • A five-tier energy hub model for multi-energy couplings is established. • A multi-objective joint planning model and its solution method is proposed. [ABSTRACT FROM AUTHOR]

The article offers information on the SF Expo held from May 18-20, 2009 in Guangzhou, China. The exposition included exhibitions for manufacturing, chemical processing, and rubber. A conference program was also running alongside the exhibition and was attended by around 2,000 of the delegates. The Institute of Metal Finishing (IMF)was offered a free of charge booth at the exposition. Information on the future events and plans of IMF is also included.