A comparative study of temperatures of direct sunlight and air temperatures in a standard screen measured at Doha International Airport - a pilot study

An observational campaign was conducted at Doha International Airport, Arabian Gulf to find out difference between air temperature in a standard screen and direct sunlight. Hourly observations recorded during July-August 1998 and June-August 1999 formed the basis of the study. Difference between screen temperature (ST) and outside temperature (OT) in respect of all hourly data in the above period from 0600 to 1800 hrs of local time have been computed and analysed. In order to examine the difference before sunrise and after sunset, observations were also made during 1900-0500 hrs of local time from 1st to 18th of July 1998. Results of the study revealed that the magnitude of the differences between OT and ST is not as high as expected. The highest difference observed was 5.1° C on 16th July 1999 at 0900 hr. As anticipated, the temperature of direct sunlight between 0600 hr and 1700 hr were always higher than the screen temperature. However, after 0500 pm of local time, the screen temperatures are found to be higher than outside temperature though the sunset time in these months are after 0600 pm. The mean difference between ST and DT in June, July and August respectively found to be 1.43° C, 1.53° C and 1.67° C. The highest difference observed in these months was 3.8° C, 5.1° C and 4.1° C respectively. The study has also indicated that the difference between OT and ST is generally higher during 0900-1000 hrs of local time and lower during two hours before sunrise and sunset.

Forecast of Hourly Airport Visibility Based on Artificial Intelligence Methods

Based on the hourly visibility data, visibility and its changes during 2010–2020 at monthly and annual time scales over 47 international airports in China are investigated, and nine artificial-intelligence-based hourly visibility prediction models are trained (hourly data in 2018–2019) and tested (hourly data in 2020) at these airports. The analyses show that the visibility of airports in eastern and central China is at a poor level all year round, and LXA (in Lhasa) has good visibility all year round. Airports in south and the northwest China have better visibility from May to October and poorer visibility from November to April. In all months, the increasing visibility mainly occurs in the central, northeast and coastal areas of China, while decreasing visibility mainly appears in the western and northern parts of China. In spring, summer and autumn, the changes difference between east and west is particularly obvious. This East–West distribution of trends is obviously different from the North–South distribution shown by the mean. For all airports, good visibility mainly occurs from 14:00–18:00 p.m. Beijing Time, while poor visibility mainly concentrates from 22:00 p.m. to 12:00 p.m. the next day, especially between 3:00–9:00 a.m. Our proposed artificial intelligence algorithm models can be reasonably used in airport visibility prediction. In particular, most algorithm models have the best results in the visibility prediction over HFE (in Hefei) and SJW (in Shijiazhuang). On the contrary, the worst forecast results appear at LXA and LHW (in Lanzhou) airports. The prediction results of airport visibility in the cold season (October–December) are better than those in the warm season (May–September). Among the algorithm models, the prediction performance of the RF-based model is the best.

DISTRIBUTION OF TEMPERATURE INDICES OF EXTREME IN THE TERRITORY OF THE EUROPEAN PART OF RUSSIA IN THE PERIOD 1980-2019

The spatio-temporal variability of the average values of temperature indices of climate extremity in the territory of the European part of Russia (ER) in 1980-2019 is presented. To calculate the extremeness indices, we used hourly data on the maximum and minimum temperatures obtained using the ERA5 reanalysis on a 1°´1° spatial grid. Statistical processing of the index values revealed an increase in the temperature indices TNX, TNN, TXN, TXX, associated with the minimum and maximum temperatures, with the exception of the north and southeast of the region. An increase in the number of sunny days and a decrease in the number of frosty days were also revealed.

A Comparison of Machine Learning Methods to Forecast Tropospheric Ozone Levels in Delhi

Ground-level ozone is a pollutant that is harmful to urban populations, particularly in developing countries where it is present in significant quantities. It greatly increases the risk of heart and lung diseases and harms agricultural crops. This study hypothesized that, as a secondary pollutant, ground-level ozone is amenable to 24 h forecasting based on measurements of weather conditions and primary pollutants such as nitrogen oxides and volatile organic compounds. We developed software to analyze hourly records of 12 air pollutants and 5 weather variables over the course of one year in Delhi, India. To determine the best predictive model, eight machine learning algorithms were tuned, trained, tested, and compared using cross-validation with hourly data for a full year. The algorithms, ranked by R2 values, were XGBoost (0.61), Random Forest (0.61), K-Nearest Neighbor Regression (0.55), Support Vector Regression (0.48), Decision Trees (0.43), AdaBoost (0.39), and linear regression (0.39). When trained by separate seasons across five years, the predictive capabilities of all models increased, with a maximum R2 of 0.75 during winter. Bidirectional Long Short-Term Memory was the least accurate model for annual training, but had some of the best predictions for seasonal training. Out of five air quality index categories, the XGBoost model was able to predict the correct category 24 h in advance 90% of the time when trained with full-year data. Separated by season, winter is considerably more predictable (97.3%), followed by post-monsoon (92.8%), monsoon (90.3%), and summer (88.9%). These results show the importance of training machine learning methods with season-specific data sets and comparing a large number of methods for specific applications.

Modeling Emissions Sources and Characterization in a Dense Low-Income Township Near Industrial Zones and Mine Dumps (Case of Tsakane, South Africa).

The sources of pollution in Tsakane township, which is situated within the City of Ekurhuleni in the province of Gauteng, South Africa, are investigated in this paper. The City of Ekurhuleni has the most industrial activities reported on South Africa's National Atmospheric Emission Inventory System (NAEIS), accounting for 40% of all listed activities in the country. The problem of suburban air pollution in South Africa is mainly associated with dense low-income areas like townships. The aim of this paper was to investigate atmospheric concentration correlation parameters, emissions roses, and probability modelling functions in order to analyse and classify significant emission sources affecting the township. Sulfur dioxide, nitrogen dioxide, ozone, and PM10 were the focus of the investigation. The probability functions for identifying and characterizing unknown or hidden sources of pollution were developed using hourly data. Furthermore, K-clustering algorithm analysis technique was used to provide graphical context for sources. PM10, ozone, sulfur dioxide, and nitrogen dioxide have all been identified as having directional pollution sources that are problematic and the results provide baseline data for a detailed understanding of current emission levels and possible sources.

Micro-climatic study and trend analysis of fog characteristics at IGI airport New Delhi using hourly data (1981-2005)

Indira Gandhi International (IGI) airport, New Delhi where near about 675 flights on an averagedepart and arrive daily, is highly susceptible to dense fog occurrences during the winter season. In the present paper, anattempt has been made for development of an intensity based fog climatological information system for December andJanuary based on hourly visibility data of 25-years (1981-2005) recorded at IGI airport. Variations and trends if any werealso analyzed along with their extreme years and dates of occurrences. Data since 1964 were also used to find climaticjumps in the trend which includes various higher visibilities of no fog conditions. Besides various vital fog climatologicalinformation generated through the present study for use in aviation, the most important finding is the alarming increasingtrend of the dense fog (< 200m) occurrences in both the months up to as high as 10-20 times from 1960s in contrast tounusual drastic reduction of higher visibility hours to as low as one thirtieth to one fiftieth of hours which were observedin 1960s. Thus, finally making IGI airport, a unique airport in the world which hardly experiences good visibilityconditions (>5000m) in both the months. By considering the unexpected huge annual growth of 30% in both air trafficand passengers that India including IGI has presently been experiencing against the global average of 6%, such visibilitytrend also confirms that present flight disruptions and passengers sufferings in winter will be aggravated more severely indays to come unless CAT-III ILS implemented fully. Finally, we have computed further number of consecutive hours,spell periodicity, most favorable climatological timing of fog onset and fog dispersal based on various intensities for usein aviation and fog forecasting.

Astroclimatic Conditions at the Hoa Lac and Nha Trang Astronomical Observatories

The paper presents the first results of astroclimatic studies at the sites of the Hoa Lac and Nha Trang astronomical observatories. Our study employs Era-5 data covering a 10-yr time period (2011–2020). An analysis of the main astroclimatic characteristic, namely, the wind speed in the upper layers of the atmosphere, was performed. We calculated space distributions of the wind speed averaged in the height bin from 100 to 200 hPa. Using hourly data on pressure levels we analyzed probability distributions of the wind speed at high-level maxima at the sites of the observatories. At the Nha Trang observatory the period with a potentially high astroclimatic conditions falls on the spring when high recurrence of weak winds is observed. At the Hoa Lac observatory the best conditions are observed in the summer and the autumn. In this period, the median wind speeds are low. Additionally, we calculated spectra of the air temperature using the Fast Fourier Transform. We analyzed the deformations of the spectra with heights in a wide range of scales. At the site of the Nha Trang Astronomical Observatory, the amplitude of daily air temperature variations in the surface layer is approximately 1.5–2.5 times smaller compared to the Hoa Lac Observatory. We showed that the low-frequency maximum in the spectra is pronounced only in the lower layers of the atmosphere.

Air Pollution Dispersion Modelling using GRAL in Area Near Coal-Steam Power Plant at Central Java

Sulfur dioxide and Nitrogen dioxide were significant emissions emitted from coal-steam power plants that may cause health problems for humans and damage the environment. Studying the SO2 and NO2 gradients in Indonesian residential communities is critical for evaluating resident's SO2 and NO2 exposure. The method developed to assist analysis of spatial SO2 and NO2 gradients on a community scale combines a mesoscale Lagrangian dispersion model with field observations around coal-steam power plants using GRAL. The objectives of this study focused on GRAL dispersion of SO2 and NO2 in an Indonesian residential community near the coal-steam power plant, with a 6 km x 8 km resolution. Analysis of this model indicates a correlation between simulation and observation, with SO2 coefficient correlation (R) within 0.5 – 0.82 and NO2 coefficient correlation (R) within 0.30 – 0.59. Model performances analyze by NMSE and FB. The SO2 model is comparable to observation data since it has a better average NMSE and FB than the NO2 model. Due to data limitation of observation collected by grab sampling instead of continuous ambient measurement system affect different respond time compared with hourly data from the model.

Impacts of Meteorological Factors, VOCs Emissions and Inter-Regional Transport on Summer Ozone Pollution in Yuncheng

Summer ozone (O3) pollution in China has become increasingly serious in recent years. This study is based on hourly data of near-surface ozone (O3) and nitrogen oxides (NOx) and volatile organic compounds (VOCs) from June to August 2020 in Yuncheng, combined with meteorological data to analyse the characteristics of O3 pollution in summer and the influence of meteorological factors, precursors, and long-range transport on O3 pollution. In this paper, the VOCs/NOx characteristic ratio method was used to explore the sensitivity of O3 generation. Backward trajectories, cluster analysis, potential source contribution factor (PSCF) analysis and concentration weight trajectory (CWT) analysis were also calculated using Trajstat software. In 2020, Yuncheng had persistent O3 pollution, with the highest concentrations in June, significantly higher than July and August. Conditions of high temperature, low relative humidity and low wind speed contribute to the O3 accumulation. VOCs are the main precursors to the local production of O3. Besides, the long-range transport analysis shows that southeast-oriented air masses are the main direction influencing summer O3 pollution. The primary potential source areas of O3 are in the central and southern part of Henan province, the north-western Anhui province, and the northern Shaanxi. In addition, northern Hubei and southwestern Shandong also influence O3 pollution in summer Yuncheng.

Comparison of Methamphetamine and MDMA Extended Access Self-administration: Acquisition, Maintenance, and Response Patterns

<p>Rationale. 3,4-methylenedioxymethamphetamine (MDMA) and methamphetamine are two amphetamine derivatives with contrasting pharmacological profiles. Therefore, self- administration profiles might be expected to reflect these differences. Objectives. This study compared the latency and proportion to acquire self-administration, maintenance of self-administration, and within-session response patterns. Methods. Rats were given extended access (8-hour daily sessions) to either methamphetamine, MDMA or vehicle self-administration over a period of 10 consecutive days. A criterion based on the performance of the vehicle control group was used to determine acquisition of reliable MDMA and methamphetamine self-administration. In conjunction, for MDMA self-administration the infusion dose was halved for each rat that achieved a total of 85mg/kg for the remaining sessions. Temporal patterns of responding were assessed using hourly data of the first day of self-administration, the day following acquisition, and the final day of self-administration. Results. A greater proportion of rats in the methamphetamine group acquired self- administration and self-administration was acquired with a shorter latency compared to the MDMA group. Responding maintained by methamphetamine on day one was high. By the third day a pattern developed that was maintained throughout testing. The greatest proportion of responding occurring within the first hour of each daily test session. A progressive escalation of intake was also observed within the methamphetamine group. Responding maintained by MDMA was low on the first day, but by day 5 responding had increased with most of the responding within the session occurring during the first three hours. On day 10 the greatest amount of responding occurred during the first hour. No escalation of intake as a function of test day was observed for MDMA self-administration.</p>