Impact Of Meteorology Research Articles

Assessment of the Meteorological Impact on Improved PM2.5 Air Quality Over North China During 2016–2019 Based on a Regional Joint Atmospheric Composition Reanalysis Data‐Set

AbstractIn the context of China's clean air policy, the meteorological impacts on improved particulate matter (PM2.5) air quality during 2016–2019 are investigated based on a four‐year high‐resolution atmospheric composition reanalysis data‐set, which has been produced by the Joint Data Assimilation System to resolve long‐term fine‐scale air quality variability over China. The reanalysis assimilates surface air quality observations using the Weather Research and Forecasting model coupled with Chemistry and an ensemble‐based assimilation algorithm, and simultaneous assimilations of meteorological observations, chemical initial conditions (ICs) and emissions are applied to help reduce the uncertainty in meteorology, ICs and the emissions inventory. Further, objective weather classification method is applied to quantitatively explore synoptic circulation pattern changes and associated PM2.5 variability over North China by using this unique reanalysis data‐set. PM2.5 reanalysis data are also investigated according to different circulation types, and results indicate that temporal and spatial variations of PM2.5 are found to be closely connected with weather and circulation patterns. The northerly types correspond to the lower PM2.5 levels, while the southerly and easterly types correspond to the higher PM2.5 concentration due to favorable local meteorological conditions. According to the quantitative evaluation on circulation pattern changes, meteorological contribution have played a positive role in improving air quality in the context of China's clean air policy during 2016–2019. This study serves as a basis for future retrospective assessments of air pollutant variation and emissions regulation measures.

Highly time-resolved characterization of carbonaceous aerosols using a two-wavelength Sunset thermal–optical carbon analyzer

Abstract. Carbonaceous aerosols have great influence on the air quality, human health and climate change. Except for organic carbon (OC) and elemental carbon (EC), brown carbon (BrC) mainly originates from biomass burning as a group of OC, with strong absorption from the visible to near-ultraviolet wavelengths, and makes a considerable contribution to global warming. Large numbers of studies have reported long-term observation of OC and EC concentrations throughout the world, but studies of BrC based on long-term observations are rather limited. In this study, we established a two-wavelength method (658 and 405 nm) applied in the Sunset thermal–optical carbon analyzer. Based on a 1-year observation, we firstly investigated the characteristics, meteorological impact and transport process of OC and EC. Since BrC absorbs light at 405 nm more effectively than 658 nm, we defined the enhanced concentrations (dEC = EC405 nm − EC658 nm) and gave the possibility of providing an indicator of BrC. The receptor model and MODIS fire information were used to identify the presence of BrC aerosols. Our results showed that the carbonaceous aerosol concentrations were the highest in winter and lowest in summer. Traffic emission was an important source of carbonaceous aerosols in Nanjing. Receptor model results showed that strong local emissions were found for OC and EC; however, dEC was significantly affected by regional or long-range transport. The dEC/OC and OC/EC ratios showed similar diurnal patterns, and the dEC/OC increased when the OC/EC ratios increased, indicating strong secondary sources or biomass burning contributions to dEC. A total of two biomass burning events both in summer and winter were analyzed, and the results showed that the dEC concentrations were obviously higher on biomass burning days; however, no similar levels of the OC and EC concentrations were found both in biomass burning days and normal days in summer, suggesting that biomass burning emissions made a great contribution to dEC, and the sources of OC and EC were more complicated. Large number of open fire counts from the northwestern and southwestern areas of the study site were observed in winter and significantly contributed to OC, EC and dEC. In addition, the nearby Yangtze River Delta area was one of the main potential source areas of dEC, suggesting that anthropogenic emissions could also be important sources of dEC. The results proved that dEC can be an indicator of BrC on biomass burning days. Our modified two-wavelength instrument provided more information than the traditional single-wavelength thermal–optical carbon analyzer and gave a new idea about the measurement of BrC; the application of dEC data needs to be further investigated.

Aggravated ozone pollution in the strong free convection boundary layer

Clarifying the relationship between meteorological factors and ozone can provide scientific support for ozone pollution prediction, but the effects of boundary layer meteorology, especially boundary layer height and turbulence, on ozone pollution are rarely studied. Here, ozone and its related meteorological factors were observed in summer in Shijiazhuang, a city with the most serious ozone pollution on the North China Plain. The forced and free convection boundary layers were classified using ground remote observations. After eliminating the forced convection condition, strong free convection conditions, exhibiting a high boundary layer height, high wind speed, strong turbulence and large-scale free convection velocity, were found to be beneficial for the aggravation of ozone pollution. Combined with the ozone profile detected by a tethered balloon, the ozone chemical budget was calculated using the differences in the column ozone concentrations between the morning and afternoon, and the results confirmed the impact of free convection intensity on ozone pollution. The change in ozone sensitivity from VOCs sensitivity to NOx sensitivity driven by strong free convection was the main reason for the deterioration of ozone pollution. This study clarified the impact of boundary layer meteorology on ozone and its sensitivity and has important practical significance for ozone pollution prevention and early warning.

Impacts of Meteorology and Emissions on O3 Pollution during 2013–2018 and Corresponding Control Strategy for a Typical Industrial City of China

The air quality of Handan, a typical industrial city in China, has been significantly improved through atmospheric pollution control, except for ozone (O3) pollution. We found that, in summer, emissions of anthropogenic volatile organic compounds (VOCs) and NOx decreased yearly in Handan, but the O3 concentration significantly declined yearly during 2013–2015, whereas it experienced worsening O3 pollution after 2015. Therefore, we used the Weather Research and Forecasting Community Multiscale Air Quality (WRF–CMAQ) modeling system to simulate the influence of the meteorological conditions and emission changes in Handan during the typical period (June) of O3 pollution in 2013–2018. For benchmarked June 2013, the results showed that the reduction of the O3 concentration in June of 2014–2016 was mainly caused by emission reduction, while in June of 2017–2018, the combined effect of changes in emissions and meteorological conditions led to aggravated O3 pollution. Sensitivity analysis indicated that combined VOCs and NOx emission controls would effectively reduce incremental O3 formation when the abatement ratio of VOCs/NOx should be no less than 0.84, and we found that VOCs reduction would continusouly bring about O3 decreases under various NOx reductions, but its positive sensitivity to O3 would become smaller with NOx reduction. However, the positive influence of NOx reduction on O3 would happen until NOx reduction exceeding 45–60%. The findings will be helpful in formulating emission control strategies for coping with O3 pollution in an industrial city.

An improved pollution forecasting model with meteorological impact using multiple imputation and fine-tuning approach

Air pollution forecasting is a significant step for air quality pollution management to mitigate pollution’s negative impact on the environment and people’s health. The data-driven forecasting model can help a better understanding of environmental air quality. The existing data-driven forecasting models usually ignore missing values, the correlations between the pollutant and meteorological factors and fail to perform temporal modeling effectively, affecting prediction accuracy. In response to these issues, we present a deep learning-based Convolutional LSTM–SDAE (CLS) model to forecast the particulate matter level, revealing the correlation between particulate matter and meteorological factors. In the proposed architecture, the k nearest neighbor (KNN) imputation technique is employed to recover the air quality dataset’s missing values. The Convolutional Long Short Term Memory (CNN–LSTM) unit identifies the vast dataset’s hidden features and performs pollutants’ temporal modeling. In addition, Bidirectional Gatted Recurrent Unit (BIGRU) is implemented as both encoder and decoder in Sparse Denoising Autoencoder, which reconstructs the CNN–LSTM model’s output in the dynamic fine-tuning layer to get robust prediction results. The experimental results in Talcher, India, and Beijing, China indicate that the model can improve forecasting accuracy and outperforms the other state of art and baseline models.

Pneumothorax and the environment: A systematic review of the impact of air pollution and meteorology, and a meta-analysis on meteorology factors

A relationship between the occurrence of spontaneous pneumothorax (SP) and meteorological factors has been observed but with contradictory results. The objectives of this systematic review was to synthesis the current body of evidence to the relationships between the occurrence of SP and environmental determinants such as meteorological factors and air pollutants. We conducted a systematic search of MEDLINE, Scopus, Cochrane Library and gray literature from inception up to 31st December 2020, to find published scientific research articles based on the following eligibility criteria: original studies and population-based articles describing the relationship between meteorological factors or air pollutants and the occurrence of SP. For the meta-analysis, studies involving a quantitative analysis of the exposure variable (atmospheric pressure, temperature, humidity and wind speed) and the impact of the occurrence of SP with comparable methodology were selected. General characteristics and methodological information for each study were assessed. The quality was evaluated according to the Newcastle-Ottawa Scale. Fourty four and 13 studies were respectively included in the qualitative and quantitative analyses. The variability of the study design with moderate quality and the different measurements of the exposure variables highlight the contrasting results. The results of the meta-analyses are in favor of a higher temperature observed in the day with SP than in the days without SP (maximum: MD 0.25 (−0.08, 0.58) p = 0.14; I2 = 26%, p = 0.21; mean: MD 0.22 (−0.15, 0.59) p = 0.24; I2 = 45%, p = 0.07). The small number of studies focused on air pollution did not allow us to conclude to determine the potential impact of exposure to air pollutants on the occurrence of SP. Meteorological conditions seem to be related to SP occurrence, especially increased temperature.

Impacts of Extreme Air Pollution Meteorology on Air Quality in China

AbstractExtreme air pollution meteorological events (EAPMEs), such as atmospheric stagnation (AS), heat wave (HW), and temperature inversion, significantly affect regional air quality. In the present study, considerably increased EAPME frequency with clear spatial variations is identified in China from 1960 to 2019. Strong air quality sensitivities to EAPMEs are identified by combing air quality and meteorological observations. We found that HW greatly affects ozone concentration and may enhance the generation of fine particles. AS showed a noticeable but opposite impact on fine particle pollution between northern and southern China owing to the wintertime heating policy. EAPMEs may increase the probability of air pollution for pollution levels greater than 2. For example, the synthetic effects of multiple EAPMEs could increase the probability of summer ozone and winter fine particle pollution in China by factors of up to 5 and 2, respectively. Based on such information, more attention may be needed on the collaborative prediction of extreme meteorological events and air pollution episodes.

Relationship between Changes over Time in Factors, Including the Impact of Meteorology on Photochemical Oxidant Concentration and Causative Atmospheric Pollutants in Kawasaki

To clarify the relationship between changes in photochemical oxidants’ (Ox) concentrations and their precursors in Kawasaki, a series of analyses were conducted using data on Ox, their precursors, nitrogen oxides (NOx) and volatile organic compounds (VOCs), and meteorology that had been monitored throughout the city of Kawasaki for 30 years from 1990 to 2019. The trend in air temperature was upward, wind speed was downward, and solar radiation was upward, indicating an increasing trend in meteorological factors in which Ox concentrations tend to be higher. Between 1990 and 2013, the annual average Ox increased throughout Kawasaki and remained flat after that. The three-year moving average of the daily peak increased until 2015, and after that, it exhibited a slight decline. The amount of generated Ox is another important indicator. To evaluate this, a new indicator, the daytime production of photochemical oxidant (DPOx), was proposed. DPOx is defined by daytime averaged Ox concentrations less the previous day’s nighttime averaged Ox concentrations. The trend in DPOx from April to October has been decreasing since around 2006, and it was found that this indicator reflects the impact of reducing emissions of NOx and VOCs in Kawasaki.

A comprehensive investigation of surface ozone pollution in China, 2015–2019: Separating the contributions from meteorology and precursor emissions

Despite the considerable reductions in primary and secondary air pollutants in China, surface ozone levels have increased in recent years. We report a trend of 3.3 ± 4.7 μg.m−3 year−1 in the annual mean maximum daily average ozone over an 8-h period (MDA8 ozone) across China between 2015 and 2019. Leveraging the Kolmogorov–Zurbenko filter method, we find that meteorology enhanced the ozone levels in Beijing–Tianjin–Hebei (BTH), the Yangtze River Delta (YRD), and the Pearl River Delta (PRD) while the reduction of solar radiation and the planetary boundary layer height accelerated ozone decreases in the Sichuan Basin (SCB) after 2017. Solar radiation and temperature increases, together with the reduction in sea level pressure, were the main contributors to enhance ozone in the YRD. They also contributed to 32% of ozone increases in BTH. Weaker meridional wind, lower relative humidity, and higher temperature escalated ozone enhancement in the PRD between 2016 and 2018. Regarding precursor emissions, NO2 long-term components showed a noticeable decline in all regions after 2017, partially due to the introduction of the most current action plan to reduce air pollutants over China in 2018. In contrast, the satellite-retrieved data suggest that VOC concentrations did not change substantially in target regions during the study period. After 2017, however, VOCs slightly increased in BTH, the YRD, and the PRD, which might be driven by temperature enhancements. Overall, the impact of meteorology on ozone variations was dominant in the YRD, the PRD, and the SCB from 2015 to 2019. Precursor emissions, however, played a leading role in ozone enhancement over the BTH. We also found that BTH and the YRD were in a transitional ozone formation regime while the PRD and the SCB tended to be more NOx-sensitive.

Impact of meteorological condition changes on air quality and particulate chemical composition during the COVID-19 lockdown

Stringent quarantine measures during the Coronavirus Disease 2019 (COVID-19) lockdown period (January 23, 2020 to March 15, 2020) have resulted in a distinct decrease in anthropogenic source emissions in North China Plain compared to the paralleled period of 2019. Particularly, 22.7% decrease in NO2 and 3.0% increase of O3 was observed in Tianjin, nonlinear relationship between O3 generation and NO2 implied that synergetic control of NOx and VOCs is needed. Deteriorating meteorological condition during the COVID-19 lockdown obscured the actual PM2.5 reduction. Fireworks transport in 2020 Spring Festival (SF) triggered regional haze pollution. PM2.5 during the COVID-19 lockdown only reduced by 5.6% in Tianjin. Here we used the dispersion coefficient to normalize the measured PM2.5 (DN-PM2.5), aiming to eliminate the adverse meteorological impact and roughly estimate the actual PM2.5 reduction, which reduced by 17.7% during the COVID-19 lockdown. In terms of PM2.5 chemical composition, significant NO3− increase was observed during the COVID-19 lockdown. However, as a tracer of atmospheric oxidation capacity, odd oxygen (Ox = NO2 + O3) was observed to reduce during the COVID-19 lockdown, whereas relative humidity (RH), specific humidity and aerosol liquid water content (ALWC) were observed with noticeable enhancement. Nitrogen oxidation rate (NOR) was observed to increase at higher specific humidity and ALWC, especially in the haze episode occurred during 2020SF, high air humidity and obvious nitrate generation was observed. Anomalously enhanced air humidity may response for the nitrate increase during the COVID-19 lockdown period.

Understanding the impact of meteorology on ozone in 334 cities of China

The daily variations of near-surface ozone (O3), one of the major air pollutants in China, depend on meteorological conditions. In this study, we analyzed the characteristics of O3 pollution and the complicated interactions among the controlling meteorological factors. In China, O3 pollution is becoming severe and high-concentration areas are expanding greatly. We found that O3 was correlated to numerous meteorological variables including air pressure, temperature, relative humidity, wind speed, wind direction, sunshine duration, evaporation, and precipitation. Using generalized additive models (GAMs), we developed predictive models to forecast the maximum daily 8 h O3 mass concentration for 334 cities in China. Different cities were influenced by different meteorological variables, with temperature (90.77%), relative humidity (68.15%), and sunshine hours (66.96%) being the top three influencing factors. In addition, we found that the influence of these meteorological factors on O3 pollution was nonlinear and impacted by the interaction between variables. Considering individual relationships between each meteorological factor and O3, the average coefficient of determination (R2) of GAMs for the 334 cities was 0.64 ± 0.16. However, considering the interaction between variables, the average R2 for 334 cities of revised GAMs increased to 0.72 ± 0.15. The R2 was significantly improved in 96.72% of the cities after considering the interactions among the meteorological factors based on a ten-fold cross-validation method. Furthermore, we found that the GAM residuals were unbiased. As an example of using and interpreting the GAM results, we focused on a case study in Hangzhou during the G20 summit in 2016. During this period, nitrogen oxides (NOx) and volatile organic compounds were reduced by 51.67% and 49.00%, respectively, because of local emission controls. Despite these changes in precursor emissions, O3 changed slightly during this period, suggesting that O3 in Hangzhou at the time was not controlled by local precursor emissions, but was impacted by meteorological conditions.

Impacts of meteorology and emission variations on the heavy air pollution episode in North China around the 2020 Spring Festival.

Based on the Weather Research and Forecasting model and the Models-3 community multi-scale air quality model (WRF-CMAQ), this study analyzes the impacts of meteorological conditions and changes in air pollutant emissions on the heavy air pollution episode occurred over North China around the 2020 Spring Festival (January to Februray 2020). Regional reductions in air pollutant emissions required to eliminate the PM2.5 heavy pollution episode are also quantified. Our results found that meteorological conditions for the Beijing-Tianjin-Hebei and surrounding “2+26” cities are the worst during the heavy pollution episode around the 2020 Spring Festival as compared with two other typical heavy pollution episodes that occurred after 2015. However, because of the substantial reductions in air pollutant emissions in the “2+26” cities in recent years, and the 32% extra reduction in emissions during January to February 2020 compared with the baseline emission levels of the autumn and winter of 2019 to 2020, the maximum PM2.5 level during this heavy pollution episode around the 2020 Spring Festival was much lower than that in the other two typical episodes. Yet, these emission reductions are still not enough to eliminate regional heavy pollution episodes. Compared with the actual emission levels during January to February 2020, a 20% extra reduction in air pollutant emissions in the “2+26” cities (or a 45% extra reduction compared with baseline emission levels of the autumn and winter of 2019 to 2020) could help to generally eliminate regionwide severe pollution episodes, and avoid heavy pollution episodes that last three or more consecutive days in Beijing; a 40% extra reduction in emissions (or a 60% extra reduction compared with baseline emission levels of the autumn and winter of 2019 to 2020) could help to generally eliminate regionwide and continuous heavy pollution episodes. Our analysis finds that during the clean period after the heavy pollution episode around the 2020 Spring Festival, the regionwide heavy pollution episode would only occur with at least a 10-fold increase in air pollutant emissions.

Regression-based flexible models for photochemical air pollutants in the national capital territory of megacity Delhi

Modelling photochemical pollutants, such as ground level ozone (O3), nitric oxide (NO) and nitrogen dioxide (NO2), in urban terrain was proven to be cardinal, chronophagous and complex. We built linear regression and random forest regression models using 4-years (2015–2018; hourly-averaged) observations for forecasting O3, NO and NO2 levels for two scenarios (1-month prediction (for January 2019) and 1-year prediction (for 2019)) — with and without the impact of meteorology. These flexible models have been developed for, both, localised (site-specific models) and combined (indicative of city-level) cases. Both models were aided with machine learning, to reduce their time-intensity compared to models built over high-performance computing. O3 prediction performance of linear regression model at the city level, under both cases of meteorological consideration, was found to be significantly poor. However, the site-specific model with meteorology performed satisfactorily (r = 0.87; RK Puram site). Further, during testing, linear regression models (site-specific and combined) for NO and NO2 with meteorology, show a slight improvement in their prediction accuracies when compared to the corresponding equivalent linear models without meteorology. Random forest regression with meteorology performed satisfactorily for indicative city-level NO (r = 0.90), NO2 (r = 0.89) and O3 (r = 0.85). In both regression techniques, increased uncertainty in modelling O3 is attributed to it being a secondary pollutant, non-linear dependency on NOx, VOCs, CO, radicals, and micro-climatic meteorological parameters. Analysis of importance among various precursors and meteorology have also been computed. The study holistically concludes that site-specific models with meteorology perform satisfactorily for both linear regression and random forest regression.

Trends in Air Pollutant Concentrations and the Impact of Meteorology in Shandong Province, Coastal China, during 2013-2019

ABSTRACT Although weather conditions significantly affect air pollutant concentrations, few quantitative studies have been conducted on the effects of long-term and seasonal changes in meteorology on air quality. Hence, in this study, the trends in Shandong Province, China, for six criteria pollutants (viz., sulfur dioxide [SO2], carbon monoxide [CO], particulate matter [PM] with an aerodynamic diameter of < 10 µm [PM10], PM with an aerodynamic diameter of < 2.5 µm [PM2.5], nitrogen dioxide [NO2], and ozone [O3]) were analyzed for the period of 2013–2019, when overall emissions of air pollutants decreased, and the Weather Research and Forecasting model coupled with Chemistry (WRF/Chem) was applied to evaluate the role of inter-annual and seasonal meteorological changes. Five of the six criteria pollutants exhibited a sharp drop in concentration until 2017 and a gradual decline afterward, with the maximum and minimum annual values occurring during winter and summer, respectively. In contrast, the level of O3 rose between 2013 and 2019 and displayed the opposite seasonal trend. Also, the diurnal concentrations of the first five criteria pollutants showed a typical bimodal distribution, whereas those of the O3 showed a typical unimodal distribution. Furthermore, a trimodal distribution was observed for the ratios between the diurnal PM2.5 and PM10 concentrations. Using 2013 as the baseline, the inter-annual meteorological changes accounted for only 3.4–18.6% of the decrease in the five criteria pollutants—with little effect on the O3—between 2015 and 2019, indicating that emission control measures drove the long-term improvement in air quality during these years. However, seasonal meteorological factors, which favored diffusion during summer and winter but accumulation during spring and autumn, played a larger role in the short term for all six species, especially during winter, when they reduced concentrations (excluding those of SO2 in 2019 and O3 altogether) by 6.5–31.0%.

Role of emissions and meteorology in the recent PM2.5 changes in China and South Korea from 2015 to 2018.

In this study, we examined the change rates of PM2.5 concentrations, aerosol optical depth (AOD), and the concentrations of PM2.5 precursors, such as SO2 and NO2, in China and South Korea using surface and satellite observations from 2015 to 2018. To quantify the impacts of the emissions and meteorology on the concentration changes, we performed a series of air quality simulations with year-specific meteorology and a fixed anthropogenic emissions inventory. The surface PM2.5 observations in China and South Korea decreased at rates of 9.1 and 4.3%/yr during the study period, respectively. The AODs from Moderate Resolution Imaging Spectroradiometer (MODIS) and Geostationary Ocean Color Imager (GOCI) also decreased faster over China than the AODs over South Korea. For the PM2.5 decrease in China, the emission impact was more significant (73%) than the meteorology impact (27%). On the contrary, in South Korea, the emissions and meteorology impacts on PM2.5 reductions were similar (51% vs 49%). The SO2 concentration over China in 2018 significantly reduced to approximately half of the level in 2015. In turn, the sulfate concentration in Baengnyeong (BN), located in a downwind pathway from China to South Korea, decreased at a rate of 0.79%/month. However, the nitrate concentration in BN showed an increasing trend due to the non-linear chemical reactions among sulfate-nitrate-ammonium. The increased nitrate compensated for the reduced PM2.5 concentration from the sulfate decrease at BN. Additionally, the number of high (>50μg/m3) PM2.5 concentration days continuously decreased in China, but the number in South Korea increased. It is noted that emission reductions in an upwind area do not guarantee corresponding air quality improvement in the downwind area when complex secondary aerosol formation processes, as well as spatiotemporal changes in meteorology, are involved in the transboundary transport of air pollutants.

Impacts of Regional Transport and Meteorology on Ground-Level Ozone in Windsor, Canada

This study investigated impacts of regional transport and meteorology on ground-level ozone (O3) in the smog season (April–September) during 1996–2015 in Windsor, Ontario, Canada. Data from five upwind stations in the US, which are within 310 km (i.e., Allen Park and Lansing in Michigan, Erie, National Trail School, and Delaware in Ohio), were included to assess the regional characteristics of O3. The five US stations showed high degrees of similarity with O3 concentrations in Windsor, with overall strong correlations (r = 0.567–0.876 for hourly O3 and r = 0.587–0.92 for 8 h max O3 concentrations) and a low degree of divergence, indicating that O3 pollution in the study area shares regional characteristics. Meteorological conditions played important roles in O3 levels in Windsor. High O3 concentrations were associated with southerly and southwesterly air mass from which polluted and hot air mass was transported and that enhanced local photochemical O3 production. In contrast, northerly flows brought in clean, cool, and dry air mass, and led to low O3 concentrations. Strong correlations were found between numbers of days with 8 h max O3 concentrations greater than 70 ppb and numbers of days with daily max temperature greater than 30 °C, as well as between daily max temperatures and 8 h max O3 concentrations. Nearly half (45%) of the high O3 days (≥90th percentile) occurred in dry tropical weather during 1996–2015, and the 90th percentile 8 h max O3 was associated with dry tropical weather. Occurrences of both southerly flow hours and dry tropical weather type in the smog season increased during the study period. If there were more hot and dry days in the next few decades due to climate change, the effect of emission control on reducing peak O3 values would be diminished. Therefore, continued regional and international efforts are essential to control precursors’ emissions and to mitigate O3 pollution in Windsor.

METEOROLOGICAL FACTORS ASSOCIATED WITH THE SPREAD OF THE COVID-19 VIRUS

SESSION TITLE: Chest Infections Posters SESSION TYPE: Original Investigation Posters PRESENTED ON: October 18-21, 2020 PURPOSE: In December 2019, a SARS-CoV-2 (Severe acute respiratory failure syndrome Coronavirus- 2) virus emerged from Wuhan, China causing SARS and other pulmonary infections (COVID-19) that rapidly spread to other parts of the world, causing a major pandemic. There is data suggesting that this virus will follow similar trends as the 2009 Influenza H1N1 and 2002 SARS-COV, due geographical and meteorological factors, being the most importants the temperature, relative humidity and wind speed. We aim to analyze the impact of geographical and meteorological factors in the incidence of COVID19. METHODS: We analyzed the frequency of COVID19 cases along geographical and meteorological data from 84 regions around the world from January to March 2020, we divided the regions if they had less than 1, 000 cases vs more than 1, 000 cases of COVID-19. The meteorological data was collected from the Climate Re-analyzer platform from the Climate Change Institute at University of Maine, the National Science foundation and Russell Grinnell Memorial Trust. The monthly incidence of the cases was collected by the Mapping 2019-nCOV tool from the Center of Systems Science and Engineering (CSSE) at Johns Hopkins University. We compare the temperature (° Celsius), latitude (° North and ° South), humidity (%) wind speed (km/h) and wind direction (0°-360°) between areas with above 1, 000 cases of COVID19 vs areas with less than 1, 000 cases. Categorical variables were compared using chi-square, and a univariate linear regression model was created. We used SPSS version 25.0 (Armonk, NY: IBM Corp.) for statistical analysis. RESULTS: We found 84 regions around the world affected by COVID-19 from January 1st to March 1st, 2020. Regions with more that 1, 000 COVID-19 cases were more likely to have lower minimum temperature (5.05°C vs 9.18°c; P: <0.05), lower maximum temperature (6.22°C vs 7.97°C; P:<0.05), no difference in wind direction, higher maximum wind speed (3.29km/h vs 2.36km/h; P:<0.05), no difference between average humidity, and to be located above 30 degrees in north latitude (32% vs 11%; P<0.05) presented more cases. See table 1 and table 2. CONCLUSIONS: Regions with more than one thousand cases were statistically significantly more likely to have minimum average temperature of -0.3 ° Celsius, maximum wind speed 14.85 km / h, humidity average of 75.65% and with a latitude greater than 30 ° north. Furthers studies with larger sample are needed to assess the geographical and meteorological impact in COVID-19 pandemic. CLINICAL IMPLICATIONS: COVID19, Pandemic, meteorological data. DISCLOSURES: no disclosure on file for Waldemar Castillo; no disclosure on file for Jose Waldemar Castillo; No relevant relationships by EMILIO CASTILLO, source=Web Response No relevant relationships by Maria Choc, source=Web Response No relevant relationships by Juan Del Cid Fratti, source=Web Response No relevant relationships by Luis Godínez, source=Web Response No relevant relationships by gabriel rios, source=Web Response No relevant relationships by Angel Soto, source=Web Response

Impacts of COVID-19 lockdown, Spring Festival and meteorology on the NO2 variations in early 2020 over China based on in-situ observations, satellite retrievals and model simulations

The lockdown measures due to COVID-19 affected the industry, transportation and other human activities within China in early 2020, and subsequently the emissions of air pollutants. The decrease of atmospheric NO2 due to the COVID-19 lockdown and other factors were quantitively analyzed based on the surface concentrations by in-situ observations, the tropospheric vertical column densities (VCDs) by different satellite retrievals including OMI and TROPOMI, and the model simulations by GEOS-Chem. The results indicated that due to the COVID-19 lockdown, the surface NO2 concentrations decreased by 42% ± 8% and 26% ± 9% over China in February and March 2020, respectively. The tropospheric NO2 VCDs based on both OMI and high quality (quality assurance value (QA) ≥ 0.75) TROPOMI showed similar results as the surface NO2 concentrations. The daily variations of atmospheric NO2 during the first quarter (Q1) of 2020 were not only affected by the COVID-19 lockdown, but also by the Spring Festival (SF) holiday (January 24–30, 2020) as well as the meteorology changes due to seasonal transition. The SF holiday effect resulted in a NO2 reduction from 8 days before SF to 21 days after it (i.e. January 17 - February 15), with a maximum of 37%. From the 6 days after SF (January 31) to the end of March, the COVID-19 lockdown played an important role in the NO2 reduction, with a maximum of 51%. The meteorology changes due to seasonal transition resulted in a nearly linear decreasing trend of 25% and 40% reduction over the 90 days for the NO2 concentrations and VCDs, respectively. Comparisons between different datasets indicated that medium quality (QA ≥ 0.5) TROPOMI retrievals might suffer large biases in some periods, and thus attention must be paid when they are used for analyses, data assimilations and emission inversions.

Quantifying the anthropogenic and meteorological influences on summertime surface ozone in China over 2012–2017

We applied the global 3-D chemical transport model GEOS-Chem to examine the anthropogenic and meteorological contributions in driving summertime (JJA) surface ozone (O3) trend in China during the Clean Air Action period 2012–2017. The model captures the observed spatial distribution of summertime O3 concentrations in China (R = 0.78) and reproduces the observed increasing trends in two most populated city clusters: North China Plain (NCP) and Yangtze River Delta (YRD). Trend of simulated maximum daily 8-h average (MDA8) O3 concentration is 0.58 ppbv yr−1 in NCP and 1.74 ppbv yr−1 in YRD in JJA 2012–2017. Sensitivity studies show that both changes in anthropogenic emissions and meteorology favored the MDA8 O3 increases in these two regions with respective contributions of 39% and 49% in NCP, and 13% and 84% in YRD. In NCP, the 49% meteorology impact includes a considerable contribution from natural emissions (19%). Changes in biogenic VOCs, soil NOx, and lightning NOx emissions are estimated to enhance MDA8 O3 in NCP with a rate of 0.14, 0.10, and 0.14 ppbv yr−1, respectively. In YRD, natural emissions made small contributions to the MDA8 O3 trend. Statistical analysis shows that higher temperatures and anomalous southerlies at 850 hPa in 2017 relative to 2012 are the two major meteorological drivers in NCP that favored the O3 increases, while weaker wind speed and lower relative humidity are those for YRD. We further examined the trend of fourth highest daily maximum 8-h average (4MDA8) O3 among a specific month that linked with extreme pollution episodes. Trends of simulated 4MDA8 O3 in NCP and YRD are 34–46% higher than those of MDA8 O3 and are found more meteorology-induced. Our results suggest an important role of meteorology in driving summertime O3 increases in China in recent years.

Meteorological impact on the COVID-19 pandemic: A study across eight severely affected regions in South America

The role of meteorological factors in the transmission of the COVID-19 still needs to be determined. In this study, the daily new cases of the eight severely affected regions in four countries of South America and their corresponding meteorological data (average temperature, maximum temperature, minimum temperature, average wind speed, visibility, absolute humidity) were collected. Daily number of confirmed and incubative cases, as well as time-dependent reproductive number (Rt) was calculated to indicate the transmission of the diseases in the population. Spearman's correlation coefficients were assessed to show the correlation between meteorological factors and daily confirmed cases, daily incubative cases, as well as Rt. In particular, the results showed that there was a highly significant correlation between daily incubative cases and absolute humidity throughout the selected regions. Multiple linear regression model further confirmed the negative correlation between absolute humidity and incubative cases. The absolute humidity is predicted to show a decreasing trend in the coming months from the meteorological data of recent three years. Our results suggest the necessity of continuous controlling policy in these areas and some other complementary strategies to mitigate the contagious rate of the COVID-19.