An Application of Artificial Neural Network to Evaluate the Influence of Weather Conditions on the Variation of PM2.5-Bound Carbonaceous Compositions and Water-Soluble Ionic Species

Previous studies have determined biomass burning as a major source of air pollutants in the ambient air in Thailand. To analyse the impacts of meteorological parameters on the variation of carbonaceous aerosols and water-soluble ionic species (WSIS), numerous statistical models, including a source apportionment analysis with the assistance of principal component analysis (PCA), hierarchical cluster analysis (HCA), and artificial neural networks (ANNs), were employed in this study. A total of 191 sets of PM2.5 samples were collected from the three monitoring stations in Chiang-Mai, Bangkok, and Phuket from July 2020 to June 2021. Hotspot numbers and other meteorological parameters were obtained using NOAA-20 weather satellites coupled with the Global Land Data Assimilation System. Although PCA revealed that crop residue burning and wildfires are the two main sources of PM2.5, ANNs highlighted the importance of wet deposition as the main depletion mechanism of particulate WSIS and carbonaceous aerosols. Additionally, Mg2+ and Ca2+ were deeply connected with albedo, plausibly owing to their strong hygroscopicity as the CCNs responsible for cloud formation.

Measurement report: The importance of biomass burning in light extinction and direct radiative effect of urban aerosol during the COVID-19 lockdown in Xi'an, China

Due to the complexity of emission sources, a better understanding of aerosol optical properties is required to mitigate climate change in China. Here, an intensive real-time measurement campaign was conducted in an urban area of China before and during the COVID-19 lockdown in order to explore the impacts of anthropogenic activities on aerosol light extinction and the direct radiative effect (DRE). The mean light extinction coefficient (bext) decreased from 774.7 ± 298.1 Mm-1 during the normal period to 544.3 ± 179.4 Mm-1 during the lockdown period. A generalised additive model analysis indicated that the large decline in bext (29.7 %) was due to sharp reductions in anthropogenic emissions. Chemical calculation of bext based on a ridge regression analysis showed that organic aerosol (OA) was the largest contributor to bext in both periods (45.1 %–61.4 %), and the contributions of two oxygenated OAs to bext increased by 3.0 %–14.6 % during the lockdown. A hybrid environmental receptor model combined with chemical and optical variables identified six sources of bext. It was found thatbext from traffic-related emissions, coal combustion, fugitive dust, the nitrate and secondary OA (SOA) source, and the sulfate and SOA source decreased by 21.4 %–97.9 % in the lockdown, whereas bext from biomass burning increased by 27.1 %, mainly driven by the undiminished need for residential cooking and heating. An atmospheric radiative transfer model was further used to illustrate that biomass burning, rather than traffic-related emissions, became the largest positive effect (10.0 ± 10.9 W m-2) on aerosol DRE in the atmosphere during the lockdown. Our study provides insights into aerosol bext and DRE from anthropogenic sources, and the results imply the importance of controlling biomass burning for tackling climate change in China in the future.

Impact of reduced anthropogenic emissions on chemical characteristics of urban aerosol by individual particle analysis.

A single particle aerosol mass spectrometer was deployed in a heavily polluted area of China during a coronavirus lockdown to explore the impact of reduced anthropogenic emissions on the chemical composition, size distributions, mixing state, and secondary formation of urban aerosols. Ten particle groups were identified using an adaptive resonance network algorithm. Increased atmospheric oxidation during the lockdown period (LP) resulted in a 42.2%-54% increase in the major NaK-SN particle fraction relative to the normal period (NP). In contrast, EC-aged particles decreased from 31.5% (NP) to 23.7% (LP), possibly due to lower emissions from motor vehicles and coal combustion. The peak particle size diameter increased from 440 nm during the NP to 500 nm during LP due to secondary particle formation. High proportions of mixed 62NO3- indicate extensive particle aging. Correlations between secondary organic (43C2H3O+, oxalate) and secondary inorganic species (62NO3-, 97HSO4- and 18NH4+) versus oxidants (Ox = O3 + NO2) and relative humidity (RH) indicate that increased atmospheric oxidation promoted the generation of secondary species, while the effects of RH were more complex. Differences between the NP and LP show that reductions in primary emissions had a remarkable impact on the aerosol particles. This study provides new insights into the effects of pollution emissions on atmospheric reactions and the specific aerosol types in urban regions.

Extrapolation of anthropogenic disturbances on hazard elements in PM2.5 in a typical heavy industrial city in northwest China.

Baoji is a typical heavy industrial city in northwest China. Its air quality is greatly impacted by the emission from the factories. Elements in fine particulate matter (PM2.5) that are greatly emitted from anthropogenic sources could pose diverse health impacts on humans. In this study, an online AMMS-100 atmospheric heavy metal analyzer was used to quantify 30 elements in PM2.5 under the weak and strong anthropogenic disturbance scenarios before the city lockdown period (from January 9th to 23rd) and the lockdown period (from January 26th to February 9th) due to the outbreak of COVID-19 in 2020. During the lockdown period, the average total concentration of total quantified elements was 3475.0 ng/m3, which was 28% and 33% lower than that of the week and strong anthropogenic disturbance scenarios during the pre-lockdown period. The greatest reductions were found for the elements of chromium (Cr), titanium (Ti), manganese (Mn), and Zinc (Zn), consistent with the industrial structure of Baoji. The mass concentrations of most elements showed obvious reductions when the government post-alerted the industries to reduce the operations and production. Dust, traffic sources, combustion, non-ferrous metal processing, and Ti-related industrial processing that are the contributors of the elements in the pre-lockdown period were apportioned by the positive matrix factorization (PMF) model. Substantial changes in the quantified elements' compositions and sources were found in the lockdown period. Health assessment was conducted and characterized by apportioned sources. The highest non-carcinogenic risk (HQ) was seen for Zn, demonstrating the high emissions from the related industrial activities. The concentration level of arsenic (As) exceeded the incremental lifetime carcinogenic risk (ILCR) in the lockdown period. This could be attributed to the traditional firework activities for the celebration of the Chinese New Year within the lockdown period.

The Heavy Particulate Matter Pollution During the COVID-19 Lockdown Period in the Guanzhong Basin, China.

Nationwide restrictions on human activities (lockdown) in China since 23 January 2020, to control the 2019 novel coronavirus disease pandemic (COVID-19), has provided an opportunity to evaluate the effect of emission mitigation on particulate matter (PM) pollution. The WRF-Chem simulations of persistent heavy PM pollution episodes from 20 January to 14 February 2020, in the Guanzhong Basin (GZB), northwest China, reveal that large-scale emission reduction of primary pollutants has not substantially improved the air quality during the COVID-19 lockdown period. Simultaneous reduction of primary precursors during the lockdown period only decreases the near-surface PM2.5 mass concentration by 11.6% (12.6 µg m-3), but increases ozone (O3) concentration by 9.2% (5.5 µg m-3) in the GZB. The primary organic aerosol and nitrate are the major contributor to the decreased PM2.5 in the GZB, with the reduction of 28.0% and 21.8%, respectively, followed by EC (10.1%) and ammonium (7.2%). The increased atmospheric oxidizing capacity by the O3 enhancement facilitates the secondary aerosol (SA) formation in the GZB, increasing secondary organic aerosol and sulphate by 6.5% and 3.3%, respectively. Furthermore, sensitivity experiments suggest that combined emission reduction of NOX and VOCs following the ratio of 1:1 is conducive to lowering the wintertime SA and O3 concentration and further alleviating the PM pollution in the GZB.

Comparison of analytical sensitivity and efficiency for SARS-CoV-2 primer sets by TaqMan-based and SYBR Green-based RT-qPCR.

The pandemic of coronavirus disease 2019 (COVID-19) continues to threaten public health. For developing countries where vaccines are still in shortage, cheaper alternative molecular methods for SARS-CoV-2 identification can be crucial to prevent the next wave. Therefore, 14 primer sets recommended by the World Health Organization (WHO) was evaluated on testing both clinical patient and environmental samples with the gold standard diagnosis method, TaqMan-based RT-qPCR, and a cheaper alternative method, SYBR Green-based RT-qPCR. Using suitable primer sets, such as ORF1ab, 2019_nCoV_N1 and 2019_nCoV_N3, the performance of the SYBR Green approach was comparable or better than the TaqMan approach, even when considering the newly dominating or emerging variants, including Delta, Eta, Kappa, Lambda, Mu, and Omicron. ORF1ab and 2019_nCoV_N3 were the best combination for sensitive and reliable SARS-CoV-2 molecular diagnostics due to their high sensitivity, specificity, and broad accessibility. KEY POINTS: • With suitable primer sets, the SYBR Green method performs better than the TaqMan one. • With suitable primer sets, both methods should still detect the new variants well. • ORF1ab and 2019_nCoV_N3 were the best combination for SARS-CoV-2 detection.

Differential health and economic impacts from the COVID-19 lockdown between the developed and developing countries: Perspective on air pollution.

It is enlightening to determine the discrepancies and potential reasons for the degree of impact from the COVID-19 control measures on air quality as well as the associated health and economic impacts. Analysis of air quality, socio-economic factors, and meteorological data from 447 cities in 46 countries indicated that the COVID-19 control measures had significant impacts on the PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 µm) concentrations in 20 (reduced PM2.5 concentrations of -7.4-29.1 µg m-3) of the selected 46 countries. In these 20 countries, the robustly distinguished changes in the PM2.5 concentrations caused by the control measures differed between the developed (95% confidence interval (CI): -2.7-5.5 µg m-3) and developing countries (95% CI: 8.3-23.2 µg m-3). As a result, the COVID-19 lockdown reduced death and hospital admissions change from the decreased PM2.5 concentrations by 7909 and 82,025 cases in the 12 developing countries, and by 78 and 1214 cases in the eight developed countries. The COVID-19 lockdown reduced the economic cost from the PM2.5 related health burden by 54.0 million dollars in the 12 developing countries and by 8.3 million dollars in the eight developed countries. The disparity was related to the different chemical compositions of PM2.5. In particular, the concentrations of primary PM2.5 (e.g., BC) in cities of developing countries were 3-45 times higher than those in developed countries, so the mass concentration of PM2.5 was more sensitive to the reduced local emissions in developing countries during the COVID-19 control period. The mass fractions of secondary PM2.5 in developed countries were generally higher than those in developing countries. As a result, these countries were more sensitive to the secondary atmospheric processing that may have been enhanced due to reduced local emissions.

Impact of COVID-19 lockdown on the optical properties and radiative effects of urban brown carbon aerosol

Intensive measurements were conducted in Xi’an, China before and during a COVID-19 lockdown period to investigate how changes in anthropogenic emissions affected the optical properties and radiative effects of brown carbon (BrC) aerosol. The contribution of BrC to total aerosol light absorption during the lockdown (13%–49%) was higher compared with the normal period (4%–29%). Mass absorption cross-sections (MAC) of specific organic aerosol (OA) factors were calculated from a ridge regression model. Of the primary OA (POA), coal combustion OA (CCOA) had the largest MACs at all tested wavelengths during both periods due to high molecular-weight BrC chromophores;that was followed by biomass burning OA (BBOA) and hydrocarbon-like OA (HOA). For secondary OA (SOA), the MACs of the less-oxidized oxygenated OA (OOA) species (LO-OOA) at λ = 370–590 nm were higher than those of more-oxidized OOA (MO-OOA) during both periods, presumably due to chromophore bleaching. The largest contributor to BrC absorption at the short wavelengths was CCOA during both periods, but BrC absorption by LO-OOA and MO-OOA became dominant at longer wavelengths during the lockdown. The estimated radiation forcing efficiency of BrC over 370–600 nm increased from 37.5 W· g-1 during the normal period to 50.2 W·g-1 during the lockdown, and that enhancement was mainly caused by higher MACs for both LO-OOA and MO-OOA. This study provides insights into the optical properties and radiative effects of source-specific BrC aerosol when pollution emissions are reduced.

Quantifying Air Pollutant Variations during COVID-19 Lockdown in a Capital City in Northwest China

In the context of the outbreak of coronavirus disease 2019 (COVID-19), strict lockdown policies were implemented to control nonessential human activities in Xi’an, northwest China, which greatly limited the spread of the pandemic and affected air quality. Compared with pre-lockdown, the air quality index and concentrations of PM2.5, PM10, SO2, and CO during the lockdown reduced, but the reductions were not very significant. NO2 levels exhibited the largest decrease (52%) during lockdown, owing to the remarkable decreased motor vehicle emissions. The highest K+ and lowest Ca2+ concentrations in PM2.5 samples could be attributed to the increase in household biomass fuel consumption in suburbs and rural areas around Xi’an and the decrease in human physical activities in Xi’an (e.g., human travel, vehicle emissions, construction activities), respectively, during the lockdown period. Secondary chemical reactions in the atmosphere increased in the lockdown period, as evidenced by the increased O3 level (increased by 160%) and OC/EC ratios in PM2.5 (increased by 26%), compared with pre-lockdown levels. The results, based on a natural experiment in this study, can be used as a reference for studying the formation and source of air pollution in Xi’an and provide evidence for establishing future long-term air pollution control policies.

A rapid screening classifier for diagnosing COVID-19.

Rationale: Coronavirus disease 2019 (COVID-19) has caused a global pandemic. A classifier combining chest X-ray (CXR) with clinical features may serve as a rapid screening approach. Methods: The study included 512 patients with COVID-19 and 106 with influenza A/B pneumonia. A deep neural network (DNN) was applied, and deep features derived from CXR and clinical findings formed fused features for diagnosis prediction. Results: The clinical features of COVID-19 and influenza showed different patterns. Patients with COVID-19 experienced less fever, more diarrhea, and more salient hypercoagulability. Classifiers constructed using the clinical features or CXR had an area under the receiver operating curve (AUC) of 0.909 and 0.919, respectively. The diagnostic efficacy of the classifier combining the clinical features and CXR was dramatically improved and the AUC was 0.952 with 91.5% sensitivity and 81.2% specificity. Moreover, combined classifier was functional in both severe and non-serve COVID-19, with an AUC of 0.971 with 96.9% sensitivity in non-severe cases, which was on par with the computed tomography (CT)-based classifier, but had relatively inferior efficacy in severe cases compared to CT. In extension, we performed a reader study involving three experienced pulmonary physicians, artificial intelligence (AI) system demonstrated superiority in turn-around time and diagnostic accuracy compared with experienced pulmonary physicians. Conclusions: The classifier constructed using clinical and CXR features is efficient, economical, and radiation safe for distinguishing COVID-19 from influenza A/B pneumonia, serving as an ideal rapid screening tool during the COVID-19 pandemic.

Impacts of primary emissions and secondary aerosol formation on air pollution in an urban area of China during the COVID-19 lockdown.

Restrictions on human activities were implemented in China to cope with the outbreak of the Coronavirus Disease 2019 (COVID-19), providing an opportunity to investigate the impacts of anthropogenic emissions on air quality. Intensive real-time measurements were made to compare primary emissions and secondary aerosol formation in Xi'an, China before and during the COVID-19 lockdown. Decreases in mass concentrations of particulate matter (PM) and its components were observed during the lockdown with reductions of 32-51%. The dominant contributor of PM was organic aerosol (OA), and results of a hybrid environmental receptor model indicated OA was composed of four primary OA (POA) factors (hydrocarbon-like OA (HOA), cooking OA (COA), biomass burning OA (BBOA), and coal combustion OA (CCOA)) and two oxygenated OA (OOA) factors (less-oxidized OOA (LO-OOA) and more-oxidized OOA (MO-OOA)). The mass concentrations of OA factors decreased from before to during the lockdown over a range of 17% to 58%, and they were affected by control measures and secondary processes. Correlations of secondary aerosols/ΔCO with Ox (NO2 + O3) and aerosol liquid water content indicated that photochemical oxidation had a greater effect on the formation of nitrate and two OOAs than sulfate; however, aqueous-phase reaction presented a more complex effect on secondary aerosols formation at different relative humidity condition. The formation efficiencies of secondary aerosols were enhanced during the lockdown as the increase of atmospheric oxidation capacity. Analyses of pollution episodes highlighted the importance of OA, especially the LO-OOA, for air pollution during the lockdown.

Temporal changes of COVID-19 pneumonia by mass evaluation using CT: a retrospective multi-center study.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has widely spread worldwide and caused a pandemic. Chest CT has been found to play an important role in the diagnosis and management of COVID-19. However, quantitatively assessing temporal changes of COVID-19 pneumonia over time using CT has still not been fully elucidated. The purpose of this study was to perform a longitudinal study to quantitatively assess temporal changes of COVID-19 pneumonia. METHODS: This retrospective and multi-center study included patients with laboratory-confirmed COVID-19 infection from 16 hospitals between January 19 and March 27, 2020. Mass was used as an approach to quantitatively measure dynamic changes of pulmonary involvement in patients with COVID-19. Artificial intelligence (AI) was employed as image segmentation and analysis tool for calculating the mass of pulmonary involvement. RESULTS: A total of 581 confirmed patients with 1,309 chest CT examinations were included in this study. The median age was 46 years (IQR, 35-55; range, 4-87 years), and 311 (53.5%) patients were male. The mass of pulmonary involvement peaked on day 10 after the onset of initial symptoms. Furthermore, the mass of pulmonary involvement of older patients (>45 years) was significantly severer (P<0.001) and peaked later (day 11 vs. day 8) than that of younger patients (≤45 years). In addition, there were no significant differences in the peak time (day 10 vs. day 10) and median mass (P=0.679) of pulmonary involvement between male and female. CONCLUSIONS: Pulmonary involvement peaked on day 10 after the onset of initial symptoms in patients with COVID-19. Further, pulmonary involvement of older patients was severer and peaked later than that of younger patients. These findings suggest that AI-based quantitative mass evaluation of COVID-19 pneumonia hold great potential for monitoring the disease progression.

Changes in air quality related to the control of coronavirus in China: Implications for traffic and industrial emissions.

Measures taken to control the disease (Covid-19) caused by the novel coronavirus dramatically reduced the number of vehicles on the road and diminished factory production. For this study, changes in the air quality index (AQI) and the concentrations of six air pollutants (PM2.5, PM10, CO, SO2, NO2, and O3) were evaluated during the Covid-19 control period in northern China. Overall, the air quality improved, most likely due to reduced emissions from the transportation and secondary industrial sectors. Specifically, the transportation sector was linked to the NO2 emission reductions, while lower emissions from secondary industries were the major cause for the reductions of PM2.5 and CO. The reduction in SO2 concentrations was only linked to the industrial sector. However, the reductions in emissions did not fully eliminate air pollution, and O3 actually increased, possibly because lower fine particle loadings led to less scavenging of HO2 and as a result greater O3 production. These results also highlight need to control emissions from the residential sector.