Temperature Inversions,Meteorological Variables and Air Pollutants and Their Influence on Acute Respiratory Disease in the Guadalajara Metropolitan Zone,Jalisco,Mexico

The presence of temperature inversions (TI), concentration of air pollutants (AP) and meteorological variables (MV) affect the welfare of the population, creating public health problems (acute respiratory diseases ARDs, among others). The Guadalajara Metropolitan Zone (GMZ) experiences high levels of air pollution, which associated with the presence of temperature inversions and meteorological variations is conducive to the incidence of ARDs in children. The aim of this work is to evaluate the TI, MV, AP and their influence on the ARDs in children under five years in the GMZ from 2003 to 2007. In this period, the moderate and strong TI are the most frequent presenting from November to May. The AP shows a variable behavior during the year and between years, with the highest concentration of particles less than 10 microns (PM10), followed by ozone (O3), nitrogen dioxide (NO2), nitrogen oxides (NOX), carbon monoxide (CO) and sulfur dioxide (SO2), the most affected areas are the southeast of the GMZ. Annual arithmetic mean is 213,510 ± 41,209 ARDs consultations. The most important diseases are acute respiratory infections (98.0%), followed by pneumonia and bronchopneumonia (1.1%), asthma and status asthmaticus (0.5%) and streptococcal pharyngitis and tonsillitis (0.4%). Months with most inquiries were from October to March, mainly in the southeast, south and center of the city, coinciding with high levels of AP. Statistical analysis shows that the TI have significant correlation with ARDs in three years, temperature (Temp) in two, relative humidity (RH) in two, wind speed (WS) in three, wind direction (WD) in two, while that air pollutants NOX and NO2 showed significant correlation with ARDs throughout the period. CO and SO2 showed significance in two years, while the PM10 and O3 in one.

Data Quality Control Method of a New Drifting Observation Technology Named Drifting Air-Sea Interface Buoy

An integral quality control(QC)procedure that integrates various QC methods and considers the design indexes and operational status of the instruments for the observations of drifting air-sea interface buoy was developed in the order of basic in-spection followed by targeted QC.The innovative method of combining a moving Hampel filter and local anomaly detection com-plies with statistical laws and physical processes,which guarantees the QC performance of meteorological variables.Two sets of observation data were used to verify the applicability and effectiveness of the QC procedure,and the effect was evaluated using the observations of the Kuroshio Extension Observatory buoy as the reference.The results showed that the outliers in the time series can be correctly identified and processed,and the quality of data improved significantly.The linear correlation between the quality-controlled observations and the reference increased,and the difference decreased.The correlation coefficient of wind speed before and after QC increased from 0.77 to 0.82,and the maximum absolute error decreased by approximately 2.8ms^(-1).In addition,air pressure and relative humidity were optimized by 10^(-3)–10^(-2) orders of magnitude.For the sea surface temperature,the weight of coefficients of the continuity test algorithm was optimized based on the sea area of data acquisition,which effectively expanded the applicability of the algorithm.

Dew amount and its long-term variation in the Kunes River Valley,Northwest China

Dew is an essential water resource for the survival and reproduction of organisms in arid and semi-arid regions.Yet estimating the dew amount and quantifying its long-term variation are challenging.In this study,we elucidate the dew amount and its long-term variation in the Kunes River Valley,Northwest China,based on the measured daily dew amount and reconstructed values(using meteorological data from 1980 to 2021),respectively.Four key results were found:(1)the daily mean dew amount was 0.05 mm during the observation period(4 July-12 August and 13 September-7 October of 2021).In 35 d of the observation period(i.e.,73%of the observation period),the daily dew amount exceeded the threshold(>0.03 mm/d)for microorganisms;(2)air temperature,relative humidity,and wind speed had significant impacts on the daily dew amount based on the relationships between the measured dew amount and meteorological variables;(3)for estimating the daily dew amount,random forest(RF)model outperformed multiple linear regression(MLR)model given its larger R^(2) and lower MAE and RMSE;and(4)the dew amount during June-October and in each month did not vary significantly from 1980 to the beginning of the 21^(st) century.It then significantly decreased for about a decade,after it increased slightly from 2013 to 2021.For the whole meteorological period of 1980-2021,the dew amount decreased significantly during June-October and in July and September,and there was no significant variation in June,August,and October.Variation in the dew amount in the Kunes River Valley was mainly driven by relative humidity.This study illustrates that RF model can be used to reconstruct long-term variation in the dew amount,which provides valuable information for us to better understand the dew amount and its relationship with climate change.

Under-ice ambient noise in the Arctic Ocean: observations at the long-term ice station

Under-ice ambient noise in the Arctic Ocean is studied using the data recorded by autonomous hydrophones at the long-term ice station during the 9th Chinese National Arctic Research Expedition.Time-frequency analysis of two 7-s-long ice-induced noise samples shows that both ice collision and ice breaking noise have many outliers in the time-domain(impulsive characteristic)and abundant frequency components in the frequency-domain.Ice collision noise lasts for several seconds while the duration of ice breaking noise is much shorter(i.e.,less than tens of milliseconds).Gaussian distribution and symmetric alpha stable(sαs)distribution are used in this paper to fit the impulsive under-ice noise.The sαs distribution can achieve better performance as it can track the heavy tails of impulsive noise while Gaussian distribution fails.This paper also analyzes the meteorological variables during the under-ice noise observation experiment and deduces that the impulsive ambient noise was caused by the combined force of high wind speed and increasing atmosphere temperature on the ice canopy.The Pearson correlation coefficients between long-term power spectral density variations of under-ice ambient noise and meteorological variables are also studied in this paper.

Estimation and Long-term Trend Analysis of Surface Solar Radiation in Antarctica: A Case Study of Zhongshan Station

Long-term,ground-based daily global solar radiation (DGSR) at Zhongshan Station in Antarctica can quantitatively reveal the basic characteristics of Earth’s surface radiation balance and validate satellite data for the Antarctic region.The fixed station was established in 1989,and conventional radiation observations started much later in 2008.In this study,a random forest (RF) model for estimating DGSR is developed using ground meteorological observation data,and a highprecision,long-term DGSR dataset is constructed.Then,the trend of DGSR from 1990 to 2019 at Zhongshan Station,Antarctica is analyzed.The RF model,which performs better than other models,shows a desirable performance of DGSR hindcast estimation with an R^2 of 0.984,root-mean-square error of 1.377 MJ m^(-2),and mean absolute error of 0.828 MJ m^(-2).The trend of DGSR annual anomalies increases during 1990–2004 and then begins to decrease after 2004.Note that the maximum value of annual anomalies occurs during approximately 2004/05 and is mainly related to the days with precipitation (especially those related to good weather during the polar day period) at this station.In addition to clouds and water vapor,bad weather conditions (such as snowfall,which can result in low visibility and then decreased sunshine duration and solar radiation) are the other major factors affecting solar radiation at this station.The high-precision,longterm estimated DGSR dataset enables further study and understanding of the role of Antarctica in global climate change and the interactions between snow,ice,and atmosphere.

Study on the Applicability of ERA5 Reanalysis Data at Lake Taihu

Lakes are an important component of the earth climate system. They play an important role in the study of basin weather forecasting, air quality forecasting, and regional climate research. The accuracy of driving variables is the basic premise to ensure the rationality of lake mode simulation. Based on the in-situ observations at Bifenggang site of the Lake Taihu Eddy flux Network from 2012 to 2017, this paper investigated temporal variations in temperature, relative humidity, wind speed, radiation components at different time scales (hourly, seasonal and interannual). ERA5 reanalysis data were compared with in-situ observation to quantify the error and evaluate the performance of reanalysis data. The results show that: 1) On the hourly scale, the ERA5 reanalysis data described air temperature, and downward long-wave radiation more accurately. 2) On the seasonal variation scale, the ERA5 reanalysis data described air temperature, and downward long-wave radiation more accurately. However, the descriptions of wind speed, relative humidity and downward short-wave have large deviations. 3) On the interannual scale, the ERA5 reanalysis data show a good performance for temperature, followed by downward longwave radiation, downward shortwave radiation and relative humidity.

An optimized approach for estimating benzene in ambient air within an air quality monitoring network

Benzene is a carcinogenic air pollutant for which European legislation has set an annual limit and criteria for the number of fixed monitoring sites within air quality networks(AQMN).However,due to the limited number of fixed sites for benzene measurement,exposure data are lacking.Considering the relationship between benzene levels and other variables monitored within an AQMN,such as NO_(2),O_(3),temperature,solar radiation,and accumulated precipitation,this study proposes an approach for estimating benzene air concentrations from the related variables.Using the data of the aforementioned variables from23 fixed stations during 2016-2017,the proposed approach was able to forecast benzene concentration for 2018 with high confidence,providing enriched data on benzene exposure and its trends.Moreover,the spatial distribution of the estimated versus the most representative benzene levels was quite similar.Finally,an artificial neural network identified the most representative fixed benzene monitoring sites within the AQMN.

Gaseous pollutant transport from an underground parking garage in a Mediterranean multi-story building—Effect of temporal resolution under varying weather conditions

Indoor air dynamics and quality in high density residential buildings can be complex as it is affected by both building parameters,pollution sources,and outdoor meteorological conditions.The present study used CONTAM simulations to investigate the intra-building transport and concentration of an inert pollutant continuously emitted from an underground garage of a 15-floor building under moderate Mediterranean weather.The effects of outdoor meteorological conditions(air temperature,wind speed and direction)on indoor distribution of the emitted pollutant was tested under constant conditions.The importance of using actual transient meteorological data and the impact of their temporal resolution on calculated concentrations and exposure levels were also investigated.Vertical profiles of air exchange rate(AER)and CO concentration were shown to be sensitive to indoor-outdoor temperature difference,which controls the extent of the stack effect and its importance relative to wind effect.Even under constant conditions,transient mode simulations revealed that the time needed for pollutant distribution to reach steady state can be quite long(>24h in some cases).The temporal resolution(Ih vs.8h)of the meteorological data input was also found to impact calculated exposure levels,in an extent that varied with time,meteorological conditions and apartment position.