Strong Wind Speed Research Articles

Analysis of Meteorological Conditions on Riverbed Dust Aerosol in Taiwan

Extremely high concentrations of dust particles are occasionally generated from the riverbeds of Taiwan, affecting the visibility and traffic safety of the local and nearby areas. The condition is most severe during the winter monsoon when surface wind is strong. This study analyzes the concentration of particulate matter of 10 µm or less (PM10), wind direction, wind speed, temperature, and humidity of riverbed stations adjacent to the Daan, Dajia, Dadu, Zhuoshui, and Beinan Rivers in Taiwan for a period of two years. The weather conditions that cause the high concentration of PM10 are classified into typhoon and non-typhoon types, and the latter type is further classified into three stages: ahead of front, ahead of anticyclone, and behind anticyclone. The associated meteorological influences of these weather types on high-concentration events in the riverbed are explored. The monitoring data show that the hourly PM10 concentration of the four riverbed stations exceeded 125 µg m−3 for 35–465 h per year, and the maximum PM10 in the Daan (and Dajia), and Zhuoshui Rivers was more than 800 µg m−3. Weather analysis showed that the extreme PM10 concentration on the riverbed was caused by weather types: typhoon and ahead of anticyclone, in which the peak hourly concentration reached average values of more than 600 and 400 µg m−3, respectively. The high PM10 caused by the typhoon type mainly occurred in October, with an average wind speed of 6 m s−1, high temperature of 25 °C, and mostly northeasterly winds. The ahead of anticyclone type mainly occurred in December, with an average wind speed of 5 m s−1, and northeasterly and northwesterly winds. Both weather types of riverbed events were observed during the daytime, especially at noon time, when strong wind speed, high temperature, and low relative humidity is favorable for riverbed dust generation. On the other hand, the main months of the high PM10 concentrations of the ahead of front and behind anticyclone stages are February and April. The peak PM10 concentrations of these two types of riverbed events are both about 300 µg m−3, but sporadic riverbed dust in these weather stages is mixed with Asian dust or pollution transported to the rivers through weak northwesterly and northeasterly winds. The high concentrations of these two types of riverbed events can occur at any time; but for the Dadu River, the high concentrations are often observed in the morning, when land breezes from the southeast bring local pollutants to the river.

Wind-tunnel estimation of mean and turbulent wind speeds within canopy layer for urban campus

The aim of this study was to clarify the effects of high-rise buildings on the mean and turbulent wind speeds within the canopy layer of an urban university campus in Malaysia. We investigated the characteristics of both mean and exceeding wind speeds by deriving probability density functions of wind speed at a selected canopy height. First, mean wind speeds were compared with those predicted by several existing models as a function of building morphological parameters. Although all empirical models predicted wind speed decreases with an increase in building density, the predicted values and our experimental data did not agree. These results imply that existing prediction models exhibit limitations in specific urban morphologies. Second, the derived probability density functions approached the normal distribution when mean wind speeds increased. In addition, peak factors of wind speeds within the canopy layer approached the values determined by the normal distribution when the mean wind speed ratios were larger than 0.5. These results indicated that strong wind speeds can be predicted by the peak factor of the normal distribution regardless of the location or wind direction using the provided mean wind speed and standard deviations of the mean when wind ratios are larger than 0.5.

Contribution of Aerosol Species to the 2019 Smoke Episodes over the East Coast of Peninsular Malaysia

Large-scale biomass burning (BB) emits large amounts of aerosols that lead to transboundary smoke events and adversely impacts human health, whilst causing societal and environmental issues. High ambient PM2.5 concentration in the year 2019 based on New Malaysia Ambient Air Quality Standard (NMAAQS) was identified as high pollution episodes, HP1 and HP2 on the east coast Peninsular Malaysia (ECPM). Meanwhile, the low PM2.5 concentration episodes are known as LP1 and LP2. The transboundary smoke events in Indochina and Indonesia are linked to HP1 (March-April) and HP2(August-September), respectively from backward trajectory and MERRA-2 model reanalysed weather data. The correlation analysis showed a significantly strong positive correlation (r) of black carbon (HP1: 0.91; HP2: 0.96), organic carbon (HP1: 0.90; HP2: 0.94), and sulphate (HP1: 0.80; HP2: 0.61) with the aerosol optical depth (AOD) levels during high pollution episodes. The synoptic weather condition and inter- monsoon in HP1 and southwest monsoon in HP2 introduce strong wind speed and favourable wind pattern that can initiate the long-range transport of high AOD and PM2.5 to the ECPM region. In conclusion, this study demystified the sources of BB emissions, the transport route of transboundary smoke events, their influence factors during different high pollution periods, and the links between aerosol species from local and non-local emissions with AOD levels and PM2.5 concentrations along the ECPM, which altogether provide crucial information on climate variability signal and can help in developing a corresponding strategy for high pollution episodes.

A Review on the Building Wind Impact through On-site Monitoring in Haeundae Marine City: 2021 12th Typhoon OMAIS Case Study

Overcrowding of high-rise buildings in urban zones change the airflow pattern in the surrounding areas. This causes building wind, which adversely affects the wind environment. Building wind can generate more serious social damage under extreme weather conditions such as typhoons. In this study, to analyze the wind speed and wind speed ratio quantitatively, we installed five anemometers in Haeundae, where high-rise buildings are dense, and conducted on-site monitoring in the event of typhoon OMAIS to determine the characteristics of wind over skyscraper towers surround the other buildings. At point M-2, where the strongest wind speed was measured, the maximum average wind speed in 1 min was observed to be 28.99 m/s, which was 1.7 times stronger than that at the ocean observatory, of 17.0 m/s, at the same time. Furthermore, when the wind speed at the ocean observatory was 8.2 m/s, a strong wind speed of 24 m/s was blowing at point M-2, and the wind speed ratio compared to that at the ocean observatory was 2.92. It is judged that winds 2–3 times stronger than those at the surrounding areas can be induced under certain conditions due to the building wind effect. To verify the degree of wind speed, we introduced the Beaufort wind scale. The Beaufort numbers of wind speed data for the ocean observatory were mostly distributed from 2 to 6, and the maximum value was 8; however, for the observation point, values from 9 to 11 were observed. Through this study, it was possible to determine the characteristics of the wind environment in the area around high-rise buildings due to the building wind effect.

Statistical analysis of low-occurrence strong wind speeds at the pedestrian level around a simplified building based on the Weibull distribution

In this study, low-occurrence strong wind speeds (LOSWSs) at the pedestrian level around a high-rise building were statistically analyzed based on the two-parameter (2W) and three-parameter (3W) Weibull distributions. A wind speed database obtained using large-eddy simulations was used. The cumulative distribution functions of the wind speeds were well fitted by 2W and 3W in most regions. In the recirculation region next to the side wall of the building, the goodness of fit of 2W and 3W was slightly worse than that in other regions owing to the bimodal distribution. Compared with 2W, the location parameter of 3W improved the fit in a relatively far range beside the building. From the distribution of the scale parameter of 3W, the separation region was found to have a wider distribution range of wind speeds than other regions. Methods for estimating LOSWSs based on statistics were also discussed. Based on the Weibull distribution, the theoretical peak factors can be estimated from the coefficient of variation (CV) (2W method) or skewness (3W method) of the wind speeds. Both methods demonstrated high accuracies for estimating the LOSWSs with exceedance probabilities q = 30%–0.1%. The relative errors were within 10% at most points for both methods. However, the 3W method was more accurate than the 2W method for q = 1% and 0.1% because skewness is a higher-order statistic than CV, which results in a closer relationship with low-occurrence values.

Application of hybrid model based on CEEMDAN, SVD, PSO to wind energy prediction.

In recent years, a series of environmental problems have come one after another under the use of traditional fossil energy, such as greenhouse effect, acid rain, haze and so on. In order to solve the environmental problems and achieve sustainable development, seeking alternative resources has become the direction of joint efforts of China and the world. As an important part of new energy, wind energy needs strong wind speed prediction support in terms of providing stable electric power. As a result, it is very important to improve the accuracy of wind speed prediction. In view of this, this paper proposes a signal processing method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) combined with singular value decomposition (SVD), and uses Elman neural network optimized by particle swarm optimization algorithm (PSO) and autoregressive integrated moving average model (ARIMA) to predict the intrinsic mode functions (IMFs). Firstly, CEEMDAN combined with SVD is used to decompose and denoise the data, and the weights and thresholds of Elman are optimized by PSO. Finally, the optimized Elman and ARIMA are used to respectively predict the processed wind speed data components, and then the final prediction results are obtained. The final prediction results show that the proposed model can improve the effect of wind speed prediction, reduce the prediction error, and provide strong support for the stable operation of wind farms and the grid connection of power plants.

Numerical simulation study of the effects of foehn winds on white head incidences in Yamagata Prefecture, Japan

AbstractNumerous studies have elucidated that white heads and chalky rice are caused by hot, dry and strong winds at night. However, these studies have only used synoptic weather charts and near‐surface observation data to confirm the role of foehn in white head occurrences. This study used a high‐resolution three‐dimensional numerical weather prediction model to show that strong foehn effects induced a significant number of white heads in rice in Yamagata Prefecture, Japan. The model accurately predicted and represented the location and timing of the observed white heads, occurring only in a limited area (87.1 ha) on the southwest side of Mt. Chokai during the nighttime. Specifically, the simulated nocturnal (transpiration forcing) continuously exceeded 30 at that location. This high was caused by high temperature (>24°C), strong wind speed (>8 m s−1) and low humidity (<65%) caused by the strong foehn winds descending over the southwest slope of the mountain. In the other areas, did not exceed 30 because either wind speed, water vapour deficit or both were insufficiently high.

Dynamics of Amphan Cyclone and associated changes in ocean, land meteorological and atmospheric parameters

The low-pressure system developed in the Bay of Bengal and the Andaman Sea during March-October, often forms tropical cyclones, depending upon the intensity widespread destruction occurs in the areas where landfall takes place along the Indian coastal region. On 20 May, 2020, tropical cyclone Amphan hit the Indian coast at Bakkhali, West Bengal, in the afternoon (1330 IST). On 19 May, 2020, the intensity strengthened into a super cyclonic storm, with a strong wind speed up to 220 km/h. This cyclone affected a large population of India and Bangladesh. More than twenty-two thousand houses were damaged and millions of people were shifted to a safe place and due to the spread of COVID-19, the rescue missions were quite challenging. The cyclone affected most of the eastern states of India, heavy rainfall occurred causing floods along the track of cyclones. Using multi-satellite, ground and Argo floats data, we have analyzed meteorological and atmospheric parameters during May 2020. Our detailed analysis shows pronounced changes in atmospheric (CO mole fraction, total ozone column) and ocean parameters (chlorophyll concentration, dissolved oxygen, salinity, sea surface and sub-surface temperature) before and after the cyclone. Changes in ocean parameters such as caused by the cyclone Amphan along its track and the atmospheric and meteorological parameters change as the cyclone moves over the land.

Simulation of the role of Caspian Sea surface temperature and air temperature on precipitation intensity in lake-effect snow

Heavy snowfall is one of the perilous atmospheric phenomena which occurs occasionally in south-west coasts of the Caspian Sea. In this paper, we have simulated the role of sea surface temperature (SST) and lake-air temperature difference on the lake-effect snow in south-west coasts of the Caspian Sea using Weather Research and Forecasting (WRF) model. SST was increased by 2 °C–4 °C and air temperature decrease by 2 °C–4 °C in model simulations and the resulting precipitation was compared to observed values. The results show that the precipitation is significantly sensitive to the SST and lake-air temperature difference. The results indicated that the regions with heavy precipitation correspond with 4 °C increase in SST and 2 °C decrease in air temperature, add about 60 mm to the total precipitation. Furthermore, the increase in SST enhances snowfall along the coastlines. In overall, an increase between 2 °C and 4 °C in SST over 5 days, increases the precipitation between 8.43% and 19.41%, respectively, and decrease air temperature by 2 °C and 4 °C increase precipitation about 24.78% and 0.72%, respectively. Overall, the highest increase in precipitation in the whole region is related to the 2 °C decrease in air temperature and the 4 °C increase in SST. Since other atmospheric parameters such as pressure, wind field, and humidity flux are affected by temperature change, so excessive decrease in air temperature tends to weaken the low pressure and vertical motion and less precipitation occurs. Therefore, the amount of the lake-air temperature difference is a key factor in strengthening the lake-effect precipitation.Additionally, SST increases the latent heat flux and injects additional humidity to the region which further strengthens orographic uplifts and intensifies unstable condition. In the meantime, stronger wind speed caused by unstable boundary layer over the lake and sufficient fetch, accumulate additional humidity in the convergence zone along the south-west coastlines.

Calibration of scatterometer wind speed under hurricane conditions

AbstractFour scatterometers, namely: METOP-A, METOP-B, ERS-2 and OCEANSAT-2 were re-calibrated against combined National Data Buoy Center (NDBC) data and aircraft Stepped Frequency Microwave Radiometer (SFMR) data from hurricanes. As a result, continuous calibration relations over the wind speed range 0 to 45 ms-1 were developed. The calibration process uses match-up criteria of 50 km and 30 min for the buoy data. However, due to the strong spatio-temporal wind speed gradients in hurricanes, a method which considers both scatterometer and SFMR data in a storm-centred translating frame of reference is adopted. The results show that although the scatterometer radar cross-section is degraded at high wind speeds, it is still possible to recover wind speed data using the re-calibration process. Data validation between the scatterometers shows that the calibration relations produce consistent results across all scatterometers and reduce the bias and root mean square error compared to previous calibrations. In addition, the results extend the useful range of scatterometer measurements to as high as 45 ms-1.

Analysis of High-Concentration PM2.5 Episodes during Winter 2019–2020 in Seoul, Korea

PM2.5 is a WHO-designated first-class carcinogen and coping with high-concentration situations with high human risk is becoming more important. In particular, Korea has a high concentration of PM2.5 in winter due to its geographical characteristics, which can be largely divided into foreign inflows and domestic atmospheric stability. To determine this, wind patterns and air pressure data were analyzed representatively and episodes about high concentration phenomena were classified. In this study, high-concentration PM2.5 episodes, where the daily average PM2.5 concentration in Seoul exceeded 35 μg/m3 between October 2019 to March 2020, were analyzed case-by-case. The criteria for the separation of consecutive episodes were established. Then, the designated episodes were classified into four categories based on cause: atmospheric stagnation, combination of causes, penetration from abroad, and accumulation. To identify the causes of the episodes, wind direction, wind speed data, wind roses, and air quality forecast modeling data obtained from “Air Korea” were studied. Fifteen episodes were identified and analyzed and each were given a classification type. Furthermore, the phenomenon of high-concentration episodes was summarized after detailed individual analysis of the episodes. As a result of case analysis, just before there was an inflow from abroad due to strong wind speed, a characteristic of low PM2.5 concentration of air quality as a kind of cleaning effect could be found. In addition, alarm-level PM2.5 concentrations of 75 μg/m3 or higher were often made by external inflow. This will contribute towards identifying the main causes of high PM2.5 concentration episodes in Korea when it is applied over a longer time period.

Exploratory investigation on the suppression efficiency of fugitive dust emitted from coal stockpile: Comparison of innovative atomizing and traditional spraying technologies

This research applied two suppression technologies of atomizing dust suppression (ADS) and spraying dust suppression (SDS) to eliminate fugitive dust emitted from coal stockpiles and further investigated their optimal operating parameters. In field tests, the mass concentrations of TSP, PM10, and PM2.5 were simultaneously measured at both upwind and downwind sites of a simulated coal stockpile to determine the suppression efficiency of fugitive dust. High concentrations of fugitive dust were observed under a strong wind speed of 10 m/s, particularly for particle sizes larger than 7 μm. Overall, the suppression efficiencies of fugitive dust by ADS (75.50–88.17 %) were always higher than those by SDS (70.70–84.50 %). Fine mist atomized by ADS covered a wider surface area of the coal stockpiles and captured more dust in the air than SDS. The suppression efficiencies of fugitive dust for intermittent watering (82.81 %) were higher than those for continuous watering (77.75 %). Intermittent watering could maintain a protective moisture layer over the entire surface of coal stockpiles, thus extending the drying time of moisture from the coal stockpiles. The optimal operating parameter for ADS was intermittent atomizing at an angle of 30°, while that for SDS was intermittent spraying at an angle of 45°.

Cloud drop number concentrations over the western North Atlantic Ocean: seasonal cycle, aerosol interrelationships, and other influential factors.

Cloud drop number concentrations (Nd) over the western North Atlantic Ocean (WNAO) are generally highest during the winter (DJF) and lowest in summer (JJA), in contrast to aerosol proxy variables (aerosol optical depth, aerosol index, surface aerosol mass concentrations, surface cloud condensation nuclei (CCN) concentrations) that generally peak in spring (MAM) and JJA with minima in DJF. Using aircraft, satellite remote sensing, ground-based in situ measurement data, and reanalysis data, we characterize factors explaining the divergent seasonal cycles and furthermore probe into factors influencing Nd on seasonal timescales. The results can be summarized well by features most pronounced in DJF, including features associated with cold-air outbreak (CAO) conditions such as enhanced values of CAO index, planetary boundary layer height (PBLH), low-level liquid cloud fraction, and cloud-top height, in addition to winds aligned with continental outflow. Data sorted into high- and low-Nd days in each season, especially in DJF, revealed that all of these conditions were enhanced on the high-Nd days, including reduced sea level pressure and stronger wind speeds. Although aerosols may be more abundant in MAM and JJA, the conditions needed to activate those particles into cloud droplets are weaker than in colder months, which is demonstrated by calculations of the strongest (weakest) aerosol indirect effects in DJF (JJA) based on comparing Nd to perturbations in four different aerosol proxy variables (total and sulfate aerosol optical depth, aerosol index, surface mass concentration of sulfate). We used three machine learning models and up to 14 input variables to infer about most influential factors related to Nd for DJF and JJA, with the best performance obtained with gradient-boosted regression tree (GBRT) analysis. The model results indicated that cloud fraction was the most important input variable, followed by some combination (depending on season) of CAO index and surface mass concentrations of sulfate and organic carbon. Future work is recommended to further understand aspects uncovered here such as impacts of free tropospheric aerosol entrainment on clouds, degree of boundary layer coupling, wet scavenging, and giant CCN effects on aerosol–Nd relationships, updraft velocity, and vertical structure of cloud properties such as adiabaticity that impact the satellite estimation of Nd.

Water-Yield Relationship and Vegetative Growth of Wonderful Young Pomegranate Trees under Deficit Irrigation Conditions in Southeastern Italy

This investigation was carried out through three successive seasons (2017, 2018, and 2019) on young pomegranate trees of the Wonderful cultivar to study the effect of four different irrigation treatments (100%, 75%, 50%, and 25% of crop evapotranspiration—ETc) on vegetative growth and the water–yield relationship. The study was conducted in Foggia (Apulia region, Southern Italy), an agricultural area characterized by strong wind speeds and scarce water resources. The results showed the effects of the different irrigation levels and seasons on the vegetative growth and fruit yield characteristics. The cumulative trunk diameter, the annual shoot growth, the number of fruits per tree, and the yield decreased from the full water restitution (100% ETc) to the severe water restriction (25% ETc). The weight and the size of fruits decreased significantly with the restriction of water volumes applied to the crop. A linear relationship between water consumption and yield (R = 1.00 in 2018 and 1.21 in 2019; n = 12) was found. The water use efficiency (WUE) gave no statistical differences among irrigation treatments. The yield response factors (Ky: 1.06 in 2018 and 0.99 in 2019) showed the sensitivity of pomegranates to water deficits.

Changes in Rumen Microbiota Affect Metabolites, Immune Responses and Antioxidant Enzyme Activities of Sheep under Cold Stimulation.

Simple SummaryUnder a cold environment, the animal’s weight is reduced and even health is affected. As we all know, microbiota is beneficial to animal health. It can produce metabolites to improve animal immunity and avoid damage. Therefore, we aimed to understand the self-protection mechanisms of sheep under cold stress. To investigate this mechanism, we designed two experiments to explore the effects of low temperature and wind speed on sheep phenotypes, rumen microbes, immune cytokines and oxidative stress. Our results identified that the sheep remained healthy in a cold environment. This may be due to the enrichment of Lachnospiraceae in the rumen. A large amount of propionate may enter into the gluconeogenesis reaction, resulting in a decrease in the content of propionate in the rumen, thereby reducing animal’s immunity. In summary, the increase of Lachnospiraceae and propionate in the rumen may help sheep live in a cold environment. Our experiments provide some direction for the healthy feeding of animals in cold environments.Low-temperature environments can strongly affect the normal growth and health of livestock. In winter, cold weather can be accompanied by strong winds that aggravate the effects of cold on livestock. In this study, two experiments were conducted to investigate the effect of low temperature and/or wind speed on physiological indices, rumen microbiota, immune responses and oxidative stress in sheep. When sheep were exposed to cold temperature and/or stronger wind speeds, the average daily gain (ADG) decreased (p < 0.05), and the abundance of Lachnospiraceae was significantly higher (p < 0.05). The acetate and propionate contents and the proportion of propionate in the rumen also significantly reduced (p < 0.05). The immunoglobulin G (IgG) and TH1-related cytokines in the blood were significantly lower (p < 0.05). However, antioxidant enzyme contents were significantly increased and the concentration of malondialdehyde (MDA) was reduced (p < 0.05). In a cold environment, the abundance of Lachnospiraceae in the rumen of sheep was highly enriched, and the decreasing of propionate might be one of the factors affecting the immunity of the animals, the sheep did not suffer from oxidative damage during the experiment.

Observed Soil Moisture Impact on Strong Convection over Mountainous Tibetan Plateau

AbstractConvection over the Tibetan Plateau (TP) has been linked to heavy rain and flooding in downstream parts of China. Understanding processes which influence the development of convection on the TP could contribute to better forecasting of these extreme events. TP scale (~1000 km) soil moisture gradients have been shown to influence formation of convective systems over the eastern TP. The importance of smaller-scale (~10 km) variability has been identified in other regions (including the Sahel and Mongolia) but has yet to be investigated for the TP. In addition, compared to studies over flat terrain, much less is known about soil moisture–convection feedbacks above complex topography. In this study we use satellite observations of cold cloud, land surface temperature, and soil moisture to analyze the effect of mesoscale soil moisture heterogeneity on the initiation of strong convection in the complex TP environment. We find that strong convection is favored over negative (positive) land surface temperature (soil moisture) gradients. The signal is strongest for less vegetation and low topographic complexity, though still significant up to a local standard deviation of 300 m in elevation, accounting for 65% of cases. In addition, the signal is dependent on background wind. Strong convective initiation is only sensitive to local (tens of kilometers) soil moisture heterogeneity for light wind speeds, though large-scale (hundreds of kilometers) gradients may still be important for strong wind speeds. Our results demonstrate that, even in the presence of complex topography, local soil moisture variability plays an important role in storm development.

Impacts of urban buildings on microclimate and cooling systems efficiency: Coupled CFD and BES simulations

The present research investigates thermal and aerodynamic effects of urban buildings on microclimate and, subsequently, on cooling systems performance. To this end, a real heterogeneous urban area in the Mediterranean climate is considered. By employing open-source tools of OpenFOAM and EnergyPlus, coupled CFD-BES simulations are conducted. In this regard, the Detailed Surface Boundary Condition Coupling (DSBCC) algorithm is proposed. It mainly focuses on meticulously detaching any patches with different thermal properties or orientation to capture their effects on cooling units. Numerical simulations are validated by performing on-site measurements. Results elucidate the wind pattern’s pivotal role in alleviating or aggravating the local Urban Heat Island (UHI) intensity. Research outcomes indicate a stabilized efficiency for split units in all situations. The effect of hot solid walls on the adjacent air is neutralized by low-temperature glass windows. On the other hand, highly irradiated roofs create thermally critical zones, where local UHI is escalated by 3.5 K and the efficiency of rooftop units (RTU) is detracted by 46.7 %. A stronger wind speed (6 instead of 2 m/s) can enhance RTUs performance by 20.7 %. Ultimately, an optimal location for RTUs can elevate their efficiency by 42.4 % and 14.4 %, regarding shadowing effect and wind ventilation, respectively. Video AbstractSupplementary data associated with this article can be found, in the online version, at https://doi.org/10.1016/j.scs.2021.102740. [Display omitted]

Air quality and health impact of 2019-20 Black Summer megafires and COVID-19 lockdown in Melbourne and Sydney, Australia.

Poor air quality is an emerging problem in Australia primarily due to ozone pollution events and lengthening and more severe wildfire seasons. A significant deterioration in air quality was experienced in Australia's most populous cities, Melbourne and Sydney, as a result of fires during the so-called Black Summer which ran from November 2019 through to February 2020. Following this period, social, mobility and economic restrictions to curb the spread of the COVID-19 pandemic were implemented in Australia. We quantify the air quality impact of these contrasting periods in the south-eastern states of Victoria and New South Wales (NSW) using a meteorological normalisation approach. A Random Forest (RF) machine learning algorithm was used to compute baseline time series' of nitrogen dioxide (NO2), ozone (O3), carbon monoxide CO and particulate matter with diameter < 2.5μm (PM2.5), based on a 19 year, detrended training dataset. Across Victorian sites, large increases in CO (188%), PM2.5 (322%) and ozone (22%) were observed over the RF prediction in January 2020. In NSW, smaller pollutant increases above the RF prediction were seen (CO 58%, PM2.5 80%, ozone 19%). This can be partly explained by the RF predictions being high compared to the mean of previous months, due to high temperatures and strong wind speeds, highlighting the importance of meteorological normalisation in attributing pollution changes to specific events. From the daily observation-RF prediction differences we estimated 249.8 (95% CI: 156.6-343.) excess deaths and 3490.0 (95% CI 1325.9-5653.5) additional hospitalisations were likely as a result of PM2.5 and O3 exposure in Victoria and NSW. During April 2019, when COVID-19 restrictions were in place, on average NO2 decreased by 21.5 and 8% in Victoria and NSW respectively. O3 and PM2.5 remained effectively unchanged in Victoria on average but increased by 20 and 24% in NSW respectively, supporting the suggestion that community mobility reduced more in Victoria than NSW. Overall the air quality change during the COVID-19 lockdown had a negligible impact on the calculated health outcomes.

The Variability of Partial Pressure of Carbon Dioxide (pCO2) in a River-Influenced Coastal Upwelling System: A Case of the Northeast Pacific Coast

The Northeast Pacific coastal ocean, as a typical river-influenced coastal upwelling system, is characterized by significant variability of sea surface partial pressure of carbon dioxide (pCO2, 1000 μatm). This study reviewed the pCO2 variability and its underlying controlling mechanism in this highly dynamic region by bringing together previous scientific findings and historical data. The large pCO2 variability reflects the complex interactions between physical processes (riverine input and coastal upwelling) and the biological responses to the nutrient transportation associated with these physical processes, while temperature and air-sea gas exchange play a minor role in affecting pCO2. Both the river water and upwelled subsurface water are characterized by higher concentrations of pCO2 and nutrients when compared to the coastal surface water. The presence of high chlorophyll-a and low pCO2 in river plumes and areas adjacent to upwelling locations showed the intense biological CO2 uptake. The influences of riverine input and coastal upwelling thus mainly depend on the competing effect of high background pCO2 of river water and upwelled subsurface water vs. the biological dropdown of pCO2 resulting from the riverine- and upwelling-associated nutrient supplies. The strength of upwelling-favorable wind plays an important role in the pCO2 variability by affecting the intensity of coastal upwelling, with stronger wind speed causing more intense upwelling. The long-term pCO2 increasing rate in the Northeast Pacific coast is observed to be lower than that in the North Pacific open ocean.

Wind induced turbulence caused colony disaggregation and morphological changes in the cyanobacterium &lt;i&gt;Microcystis&lt;/i&gt;

微囊藻群体大小和形态决定其垂向迁移能力，从而影响着水华的形成.为了探讨湖泊中风生紊流对微囊藻群体大小和形态的影响，本研究于2012年8月26日至9月7日在太湖梅梁湾的围隔内进行了12 d的昼夜不间断的高频采样（采样间隔每2小时一次）.研究期间，水面微囊藻密度呈现4次周期性消涨，藻密度变化范围为4×10⁴~2671×10⁴ cells/mL.而整个水柱中的藻密度变化范围仅为3×10⁴~18×10⁴ cells/mL.皮尔逊相关性分析表明微囊藻的原位生长速率与表面藻密度呈负相关而与风速呈正相关.强风速使微囊藻在水柱中均匀分散，增强了透光性，促进了微囊藻的生长.微囊藻群体粒径随着风速的增大逐渐减小，反之亦然.其中值粒径（D₅₀）变化范围为66.2~768.0 μm.在此期间微囊藻群体形态主要以鱼害微囊藻、不规则的惠氏微囊藻、球状的惠氏微囊藻和铜绿微囊藻群体形态为主，其占比也呈现出波动状态.皮尔逊相关分析结果显示微囊藻群体大小与风速呈负相关，说明湖泊中风生紊流会影响微囊藻群体大小.当紊流强度为2.33×10^-5 m²/s³时，微囊藻群体会发生破碎现象，该紊流强度相当于5 m/s的风在30 m深的水库或湖泊中所产生的紊流强度.微囊藻群体被风生紊流破碎后最大粒径与该风速下紊流的最小涡旋尺度相近，表明紊流的最小涡旋尺度决定了微囊藻所能形成群体的最终大小.监测期间，整水柱中不同群体形态的微囊藻占比发生了明显变化，在监测初期以鱼害微囊藻群体形态为主，随后不规则的惠氏微囊藻和铜绿微囊藻群体形态的比例不断增加，最后鱼害微囊藻群体形态又占据主导地位.球状的惠氏微囊藻群体形态在整个监测期中的比例随时间的增加而逐渐降低.不同群体形态微囊藻之间比例的大幅变化无法用微囊藻生长演替来解释.而皮尔逊相关分析结果显示鱼害微囊藻与惠氏微囊藻（不规则的和球状的惠氏微囊藻之和）群体形态之间存在负相关，且惠氏微囊藻与铜绿微囊藻群体形态呈负相关.但在今后研究中需进一步关注在微囊藻群体形态的动态变化过程中细胞大小、胶被、产毒特性和基因序列等特征，从而验证不同种微囊藻群体是否存在形态转换这一猜想.总而言之，普通强度的风生紊流能够破碎微囊藻群体，而气候变化导致的内陆湖泊周边风速下降会促使微囊藻形成更大的群体，从而有利于水华的形成.;Colony size and morphology influence the vertical migration capacity of Microcystis and therewith the occurrence of surface accumulations or blooms. To explore the influence of wind-induced turbulence on the colony size and morphology of Microcystis in field conditions, a high-frequency field investigation was conducted in an enclosure in Meiliang Bay, Lake Taihu, China, from 26 August to 7 September 2012. A Pearson's correlation analysis indicated that the in situ growth rate of Microcystis was negatively related to surface cell density and positively related to wind speed. Strong wind speed stimulated Microcystis growth by enhancing light transmission due to the homodispersion of Microcystis in the water column. The Microcystis colony size was negatively correlated with wind speed, suggesting that wind-induced turbulence could break up colonies in shallow lakes. The results indicated that Microcystis colonies could be broken up by a turbulence intensity of 2.33×10^-5 m²/s³, which corresponds to an average wind speed of 5.00 m/s in a reservoir with 30-m depth. Different Microcystis morphotypes were present and negative relationships were detected between the proportion of Microcystis ichthyoblabe and the proportion of Microcystis wesenbergii and between the proportion of Microcystis aeruginosa and the proportion of Microcystis wesenbergii out of all Microcystis throughout the water column, but more evidence is need to support the hypothesis that the morphology of Microcystis colonies changes over time. Altogether, the results suggest that declining wind speed, driven by climate change, will promote surface blooms of Microcystis due to the formation of larger colonies.