Relationships between Vertical Temperature Gradients and PM10 Concentrations during Selected Weather Conditions in Upper Silesia (Southern Poland)

This paper studies surface air temperature inversions and their impact on air pollution under the background of meteorological conditions in southern Poland. The relationship of temperature gradients and air quality classes with weather conditions in the most urbanized and polluted part of Poland as represented by the Upper Silesia region (USR) within the administrative boundaries of the Górnośląsko-Zagłębiowska Metropolis (GZM) is presented. Based on probability analysis this study hierarchized the role of the selected weather elements in the development of surface-based temperature inversion (SBI) and air quality (AQ). The thresholds of weather elements for a rapid increase in the probability of oppressive air pollution episodes were distinguished. Although most SBI occurred in summer winter SBIs were of great importance. In that season a bad air quality occurred during >70% of strong inversions and >50% of moderate inversions. Air temperature more strongly triggered AQ than SBI development. Wind speed was critical for SBI and significant for AQ development. A low cloudiness favored SBI occurrence altered air quality in winter and spring during SBI and favored very bad AQ5 (>180 µg/m3) occurrence. The probability of high air pollution enhanced by SBI rapidly increased in winter when the air temperature dropped below −6 °C the wind speed decreased below 1.5 m/s and the sky was cloudless. Changes in the relative humidity did not induce rapid changes in the occurrence of bad AQ events during SBI

Estimation of Fracture Properties by Combining DAS and DTS Measurements

Distributed Temperature Sensing (DTS) and Distributed Acoustic Sensing (DAS) measurements during hydraulic fracturing treatments are used to estimate fluid volume distribution among perforation clusters. DAS is sensitive to the acoustic signal induced by fluid flow in the near-well region during pumping a stage, while DTS is sensitive to temperature variation caused by fluid flow inside the wellbore and in the reservoir. Raw acoustic signal has to be transferred to frequency band energy (FBE) which is defined as the integration of the squared raw measurements in each DAS channel location for a fixed period of time. In order to be used in further interpretation, FBE has to be averaged between several fiber-optic channels for each cluster on each time step. Based on this input, DAS allows us to consider fluid flow through perforation stage by stage during an injection period, and to evaluate the volume of fluid pumped in each cluster location as a function of time, and therefore to estimate the cumulative volume of fluid injected into each cluster. This procedure is based on a lab-derived and computational dynamics model confirmed correlation between the acoustic signal and the flow rate. At each time step, we apply the perforation/fracture noise correlation to determine the flow rate into each cluster, constrained by the requirement that the sum of the flow rates into individual clusters must equal the total injection rate at that time. On the other hand, the DTS interpretation method is based on the transient temperature behavior during the fracturing stimulation. During injection, the temperature of the reservoir surrounding the well is cooled by the injection fluid inside the well. After shut-in of stage pumping, temperature recovers at a rate depending on the injected volume of fluid at the location. The interpretation procedure is based on the temperature behavior during the warm-back period. This temperature distribution is obtained by solution of a coupled 3-D reservoir thermal model with 1-D wellbore thermal model iteratively. Once we confirm that the DAS and DTS interpretation methods provide comparable results of the fluid volume distribution, either of the interpretation results can be used as a known input parameter for the other interpretation method to estimate additional unknown such as one of the fracture properties. In this work, the injected fluid volume distribution obtained by the DAS interpretation is used as an input parameter for a forward model which computes the temperature profile in the reservoir. By conducting temperature inversion to reproduce the temperature profile that matches the measured temperature with the fixed injection rate for each cluster, we can predict distribution of injected fluid for hydraulic fractures along a wellbore. The temperature inversion shows that multiple fractures are created in a swarm pattern from each perforation cluster with a much tighter spacing than the cluster spacing. The field data from MIP-3H provided by the Marcellus Shale Energy and Environmental Laboratory is used to demonstrate the DAS/DTS integrated interpretation method. This approach can be a valuable means to evaluate the fracturing treatment design and further understand the field observation of hydraulic fractures.

Research on a Measurement Method for Middle-Infrared Radiation Characteristics of Aircraft

Aiming at the problem wherein temperature inversion accuracy is unstable due to the major differences in atmospheric transmittance under various observation paths, a method for measuring radiation characteristics of an aircraft engine’s hot parts and skin using a cooled middle-wave infrared camera is proposed. Based on the analysis of the aircraft’s infrared radiation characteristics, the atmospheric transmission model of any observation path was revised, the absolute radiation correction model was established, and the temperature inversion equation was calculated. Then, we used the quasi-Newton method to calculate the skin temperature and discussed uncertainty sources. After the theoretical study, an outfield test was carried out. A middle-wave infrared camera with a wavelength of 3.7–4.8 μm was applied to the actual experimental observation of the turbofan civil aviation aircraft. The ground observation distance was 15 km, and the flying height was 3 km. When implementing temperature inversion with the method presented in this paper, the surface temperature of the aircraft engine hot parts was 381 K, the correction uncertainty was ±10 K, the surface temperature of the skin was 296 K, and the correction uncertainty was ±6 K. As the experiment showed, the method in this paper can effectively implement infrared target temperature inversion and provide a reference for the quantification of infrared data.

Annual and seasonal changes of the air temperature with altitude in the Upper Dades valley, High Atlas, Morocco

The purpose of this study is to determine the size of air temperature changes with altitude in the mountains of the arid zone, on the example of the Upper Dades valley (High Atlas, Morocco). The air temperature change with altitude was determined on the basis of 5 years data from three meteorological stations. The analysis was carried out on an annual and seasonal basis. The annual and daily variations of thermal gradients between pairs of stations were also determined. It was found that the average thermal gradient in the Upper Dades valley was -1.02°C per 100 m. The highest values of the thermal gradient occur in winter and the lowest in summer. In winter, the thermal gradient was characterized by the greatest variability. Minima of the daily variation of air temperature gradients were observed in early morning hours and maxima around midday. In the lower part of the valley, air temperature inversion frequently developed between 10 AM and 3 PM UTC. The obtained results show high thermal gradients in the mountains of the arid zone, with their annual amplitude increasing in the lower parts of the valley. The instantaneous values of the gradients were significantly modified by the supply of latent heat and the occurrence of dust storms. It has been shown that the advection factor plays an important role in shaping large gradient values. The study contains novel results of thermal gradient measurements in high mountains of arid zone.

Improving PM2.5 Concentration Forecast with the Identification of Temperature Inversion

The rapid development of industrialization and urbanization has had a substantial impact on the increasing air pollution in many populated cities around the globe. Intensive research has shown that ambient aerosols, especially the fine particulate matter PM2.5, are highly correlated with human respiratory diseases. It is critical to analyze, forecast, and mitigate PM2.5 concentrations. One of the typical meteorological phenomena seducing PM2.5 concentrations to accumulate is temperature inversion which forms a warm-air cap to blockade the surface pollutants from dissipating. This paper analyzes the meteorological patterns which coincide with temperature inversion and proposes two machine learning classifiers for temperature inversion classification. A separate multivariate regression model is trained for the class with or without manifesting temperature inversion phenomena, in order to improve PM2.5 forecasting performance. We chose Puli township as the studied site, which is a basin city easily trapping PM2.5 concentrations. The experimental results with the dataset spanning from 1 January 2016 to 31 December 2019 show that the proposed temperature inversion classifiers exhibit satisfactory performance in F1-Score, and the regression models trained from the classified datasets can significantly improve the PM2.5 concentration forecast as compared to the model using a single dataset without considering the temperature inversion factor.

The Innovative Method of Purifying Polluted Air in the Region of an Inversion Layer

Formation of the inversion layer causes a lack of vertical movement of the atmosphere and the occurrence of long-lasting high concentrations of pollution. The new invention makes use of shock waves, created by explosions of a mixture of flammable gases and air. These shock waves destroy the structure of the temperature inversion layer in the atmosphere and restore natural convection. Restoring vertical movements within the atmosphere causes a reduction in air pollution at the ground level. The system was tested at full technical scale in the environment. Preliminary effects indicate an average 24% reduction in PM10 concentration in the smog layer at ground level up to 20 m, with the device operating in 11-min series consisting of 66 explosions. It was also shown that the device is able to affect a larger area, at least 4 km2.

The Characterization of Haze and Dust Processes Using MAX-DOAS in Beijing, China

Haze and dust pollution have a significant impact on human production, life, and health. In order to understand the pollution process, the study of these two pollution characteristics is important. In this study, a one-year observation was carried out at the Beijing Southern Suburb Observatory using the MAX-DOAS instrument, and the pollution characteristics of the typical haze and dust events were analyzed. First, the distribution of aerosol extinction (AE) and H2O concentrations in the two typical pollution events were studied. The results showed that the correlation coefficient (r) between H2O and AE at different heights decreased during dust processes and the correlation slope (|k|) increased, whereas r increased and |k| decreased during haze periods. The correlation slope increased during the dust episode due to low moisture content and increased O4 absorption caused by abundant suspended dry crustal particles, but decreased during the haze episode due to a significant increase of H2O absorption. Secondly, the gas vertical column density (VCD) indicated that aerosol optical depth (AOD) increased during dust pollution events in the afternoon, while the H2O VCD decreased; in haze pollution processes, both H2O VCD and AOD increased. There were significant differences in meteorological conditions during haze (wind speed (WD) was <2 m/s, and relative humidity (RH) was >60%) and dust pollution (WD was >4 m/s, and RH was <60%). Next, the vertical distribution characteristics of gases during the pollution periods were studied. The AE profile showed that haze pollution lasted for a long time and changed slowly, whereas the opposite was true for dust pollution. The pollutants (aerosols, NO2, SO2, and HCHO) and H2O were concentrated below 1 km during both these typical pollution processes, and haze pollution was associated with a strong temperature inversion around 1.0 km. Lastly, the water vapor transport fluxes showed that the water vapor transport from the eastern air mass had an auxiliary effect on haze pollution at the observation location. Our results are of significance for exploring the pollution process of tropospheric trace gases and the transport of water vapor in Beijing, and provide a basis for satellite and model verification.

Dominant climate drivers of wintertime severe particulate pollution in North China

Severe particulate pollution days (SPPDs, characterized by a daily mean PM2.5 concentration exceeding 150 μg m-3), which are extremely harmful to human health and the environment, occurred frequently in North China during the boreal winters of 2013–2019. SPPDs generally occur under conducive weather patterns (CWPs) characterized by a weakened East Asian Trough in the mid-troposphere, reduced winter northerlies in the lower troposphere, and a temperature inversion at the surface. The occurrence of CWPs has been attributed to variations in numerous climate factors (e.g., Arctic sea ice cover, sea surface temperature, and atmospheric teleconnections), but the dominant climate drivers remain inconclusive. Here, we show that the East Atlantic-West Russia (EA/WR) teleconnection pattern and the Victoria Mode (VM) of sea surface temperature anomalies are the first and second dominant climate drivers, respectively, leading to CWPs in North China through the zonal and meridional propagations of Rossby waves and explaining 36.3% and 18.5%, respectively, of the observed wintertime SPPDs in Beijing-Tianjin-Hebei. Our results suggest that, with the help of seasonal forecast from climate models, the indices of the EA/WR and VM can be used to predict wintertime SPPDs over Beijing-Tianjin-Hebei.

Childhood Rotavirus Infection Associated with Temperature and Particulate Matter 2.5 µm: A Retrospective Cohort Study

No study has ever investigated how ambient temperature and PM2.5 mediate rotavirus infection (RvI) in children. We used insurance claims data from Taiwan in 2006–2012 to evaluate the RvI characteristics in children aged ≤ 9. The RvI incidence rates were higher in colder months, reaching the highest in March (117.0/100 days), and then declining to the lowest in July (29.2/100 days). The age–sex-specific average incident cases were all higher in boys than in girls. Stratified analysis by temperature (<20, 20–24, and ≥25 °C) and PM2.5 (<17.5, 17.5–31.4, 31.5–41.9, and ≥42.0 μg/m3) showed that the highest incidence was 16.4/100 days at average temperatures of <20 °C and PM2.5 of 31.5–41.9 μg/m3, with Poisson regression analysis estimating an adjusted relative risk (aRR) of 1.26 (95% confidence interval (CI) = 1.11–1.43), compared to the incidence at the reference condition (<20 °C and PM2.5 < 17.5 μg/m3). As the temperature increased, the incident RvI cases reduced to 4.84 cases/100 days (aRR = 0.40, 95% CI = 0.35–0.45) when it was >25 °C with PM2.5 < 17.5 μg/m3, or to 9.84/100 days (aRR = 0.81, 95% CI = 0.77–0.93) when it was >25 °C with PM2.5 > 42 μg/m3. The seasonal RvI is associated with frequent indoor personal contact among children in the cold months. The association with PM2.5 could be an alternative assessment due to temperature inversion.

Are elevated moist layers a blind spot for hyperspectral infrared sounders? A model study

The ability of the hyperspectral satellite-based passive infrared (IR) instrument IASI to resolve elevated moist layers (EMLs) within the free troposphere is investigated. EMLs are strong moisture anomalies with significant impact on the radiative heating rate profile and typically coupled to freezing level detrainment from convective cells in the tropics. A previous case study by Stevens et al. (2017) indicated inherent deficiencies of passive satellite-based remote sensing instruments in resolving an EML. In this work, we first put the findings of Stevens et al. (2017) into the context of other retrieval case studies of EML-like structures, showing that such structures can in principle be retrieved, but retrievability depends on the retrieval method and the exact retrieval setup. To approach a first more systematic analysis of EML retrievability, we introduce our own basic optimal estimation (OEM) retrieval, which for the purpose of this study is based on forward-modelled (synthetic) clear-sky observations. By applying the OEM retrieval to the same EML case as Stevens et al. (2017), we find that a lack of independent temperature information can significantly deteriorate the humidity retrieval due to a strong temperature inversion at the EML top. However, we show that by employing a wider spectral range of the hyperspectral IR observation, this issue can be avoided and EMLs can generally be resolved. We introduce a new framework for the identification and characterization of moisture anomalies, a subset of which are EMLs, to specifically quantify the retrieval's ability to capture moisture anomalies. The new framework is applied to 1288 synthetic retrievals of tropical ocean short-range forecast model atmospheres, allowing for a direct statistical comparison of moisture anomalies between the retrieval and the reference dataset. With our basic OEM retrieval, we find that retrieved moisture anomalies are on average 17 % weaker and 15 % thicker than their true counterparts. We attribute this to the retrieval smoothing error and the fact that rather weak and narrow moisture anomalies are most frequently missed by the retrieval. Smoothing is found to also constrain the magnitude of local heating rate extremes associated with moisture anomalies, particularly for the strongest anomalies that are found in the lower to mid troposphere. In total, about 80 % of moisture anomalies in the reference dataset are found by the retrieval. Below 5 km altitude, this fraction is only of the order of 52 %. We conclude that the retrieval of lower- to mid-tropospheric moisture anomalies, in particular of EMLs, is possible when the anomaly is sufficiently strong and its thickness is at least of the order of about 1.5 km. This study sets the methodological basis for more comprehensively investigating EMLs based on real hyperspectral IR observations and their operational products in the future.