scholarly journals Evaluation of the Influence between Local Meteorology and Air Quality in Beijing Using Generalized Additive Models

Atmosphere ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 24
Author(s):  
Kun Hou ◽  
Xia Xu
Keyword(s):  
Air Quality ◽  
Relative Humidity ◽  
Air Pollutants ◽  
Generalized Additive Models ◽  
Explanatory Power ◽  
Additive Model ◽  
Additive Models ◽  
Meteorological Variables ◽  
Marginal Effect ◽  
High Concentration

Previous studies have confirmed the inextricable connection between meteorological factors and air pollutants. This study presents the complex nonlinear relationship between meteorological variables and four major air pollutants under high-concentration air pollution in Beijing. The generalized additive model combined with marginal effects is used for quantitative analysis. After controlling the confounding factors such as long-term trends, seasonality and spatio-temporal deviation, the final fitting results exhibit that temperature, relative humidity and visibility are the most significant meteorological variables associating with PM2.5 concentration, and the marginal effect reaches 80%, −23% and 270%, respectively. Temperature and relative humidity are the most significant variables for SO2, and the marginal effect reaches 15% and 7%. The most significant variables for O3 are temperature and solar radiation, with marginal effect of up to 70% and 8%. Atmospheric pressure and temperature results in a positive effect on CO, and the marginal effect can reach 18% and 80%. All these indicate that local meteorological variables are a significant driving factor for air quality in Beijing. Other variables, such as wind speed, visibility, and precipitation, display some influence on air pollutants, but have less explanatory power in the model. Overall, our study provides a better understanding of the relationship between local meteorological variables and air quality, as well as an insight into how climate change affects air quality.

scholarly journals Modeling tropospheric ozone and particulate matter in Tunis, Tunisia using generalized additive model

2021 ◽  
Vol 31 (2) ◽  
Author(s):  
Zouhour Hammouda ◽  
Leila Hedhili Zaier ◽  
Nadege Blond
Keyword(s):  
Particulate Matter ◽  
Tropospheric Ozone ◽  
Generalized Additive Models ◽  
Additive Model ◽  
Meteorological Condition ◽  
Additive Models ◽  
Linear Functions ◽  
Meteorological Variables ◽  
Time Variables ◽  
The Impact

The main purpose of this paper is to analyze the sensitivity of tropospheric ozone and particulate matter concentrations to changes in local scale meteorology with the aid of meteorological variables (wind speed, wind direction, relative humidity, solar radiation and temperature) and intensity of traffic using hourly concentration of NOX, which are measured in three different locations in Tunis, (i.e. Gazela, Mannouba and Bab Aliwa). In order to quantify the impact of meteorological conditions and precursor concentrations on air pollution, a general model was developed where the logarithm of the hourly concentrations of O3 and PM10 were modeled as a sum of non-linear functions using the framework of Generalized Additive Models (GAMs). Partial effects of each predictor are presented. We obtain a good fit with R² = 85% for the response variable O3 at Bab Aliwa station. Results show the aggregate impact of meteorological variables in the models explained 29% of the variance in PM10 and 41% in O3. This indicates that local meteorological condition is an active driver of air quality in Tunis. The time variables (hour of the day, day of the week and month) also have an effect. This is especially true for the time variable “month” that contributes significantly to the description of the study area.

Satellite UV-Vis spectroscopy: implications for air quality trends and their driving forces in China during 2005–2017

2020 ◽  
Author(s):  
Chengxin Zhang ◽  
Cheng Liu ◽  
Qihou Hu ◽  
Zhaonan Cai ◽  
Wenjing Su ◽  
...  
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Driving Forces ◽  
Generalized Additive Models ◽  
Eastern China ◽  
Temporal Trends ◽  
Additive Models ◽  
Ozone Monitoring Instrument ◽  
Vis Spectroscopy ◽  
Ambient Concentrations

<p>Abundances of a range of air pollutants can be inferred from satellite UV-Vis spectroscopy measurements by using the unique absorption signatures of gas species. Here, we implemented several spectral fitting methods to retrieve tropospheric NO<sub>2</sub>, SO<sub>2</sub>, and HCHO from the ozone monitoring instrument (OMI), with radiative simulations providing necessary information on the interactions of scattered solar light within the atmosphere. We analyzed the spatial distribution and temporal trends of satellite-observed air pollutants over eastern China during 2005–2017, especially in heavily polluted regions. We found significant decreasing trends in NO<sub>2</sub> and SO<sub>2</sub> since 2011 over most regions, despite varying temporal features and turning points. In contrast, an overall increasing trend was identified for tropospheric HCHO over these regions in recent years. Furthermore, generalized additive models were implemented to understand the driving forces of air quality trends in China and assess the effectiveness of emission controls. Our results indicated that although meteorological parameters, such as wind, water vapor, solar radiation and temperature, mainly dominated the day-to-day and seasonal fluctuations in air pollutants, anthropogenic emissions played a unique role in the long-term variation in the ambient concentrations of NO<sub>2</sub>, SO<sub>2</sub>, and HCHO in the past 13 years. Generally, recent declines in NO<sub>2</sub> and SO<sub>2</sub> could be attributed to emission reductions due to effective air quality policies, and the opposite trends in HCHO may urge the need to control anthropogenic volatile organic compound (VOC) emissions.</p>

scholarly journals Satellite UV-Vis spectroscopy: implications for air quality trends and their driving forces in China during 2005–2017

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Chengxin Zhang ◽  
Cheng Liu ◽  
Qihou Hu ◽  
Zhaonan Cai ◽  
Wenjing Su ◽  
...  
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Driving Forces ◽  
Generalized Additive Models ◽  
Eastern China ◽  
Temporal Trends ◽  
Additive Models ◽  
Ozone Monitoring Instrument ◽  
Vis Spectroscopy ◽  
Ambient Concentrations

Abstract Abundances of a range of air pollutants can be inferred from satellite UV-Vis spectroscopy measurements by using the unique absorption signatures of gas species. Here, we implemented several spectral fitting methods to retrieve tropospheric NO2, SO2, and HCHO from the ozone monitoring instrument (OMI), with radiative simulations providing necessary information on the interactions of scattered solar light within the atmosphere. We analyzed the spatial distribution and temporal trends of satellite-observed air pollutants over eastern China during 2005–2017, especially in heavily polluted regions. We found significant decreasing trends in NO2 and SO2 since 2011 over most regions, despite varying temporal features and turning points. In contrast, an overall increasing trend was identified for tropospheric HCHO over these regions in recent years. Furthermore, generalized additive models were implemented to understand the driving forces of air quality trends in China and assess the effectiveness of emission controls. Our results indicated that although meteorological parameters, such as wind, water vapor, solar radiation and temperature, mainly dominated the day-to-day and seasonal fluctuations in air pollutants, anthropogenic emissions played a unique role in the long-term variation in the ambient concentrations of NO2, SO2, and HCHO in the past 13 years. Generally, recent declines in NO2 and SO2 could be attributed to emission reductions due to effective air quality policies, and the opposite trends in HCHO may urge the need to control anthropogenic volatile organic compound (VOC) emissions.

scholarly journals Predicting Malaria Transmission Dynamics in Dangassa, Mali: A Novel Approach Using Functional Generalized Additive Models

2020 ◽  
Vol 17 (17) ◽  
pp. 6339
Author(s):  
François Freddy Ateba ◽  
Manuel Febrero-Bande ◽  
Issaka Sagara ◽  
Nafomon Sogoba ◽  
Mahamoudou Touré ◽  
...  
Keyword(s):  
Wind Speed ◽  
Malaria Incidence ◽  
Generalized Additive Models ◽  
Community Health Center ◽  
Additive Model ◽  
Additive Models ◽  
Functional Regression ◽  
Novel Approach ◽  
Malaria Incidence Rate ◽  
Mean Wind Speed

Mali aims to reach the pre-elimination stage of malaria by the next decade. This study used functional regression models to predict the incidence of malaria as a function of past meteorological patterns to better prevent and to act proactively against impending malaria outbreaks. All data were collected over a five-year period (2012–2017) from 1400 persons who sought treatment at Dangassa’s community health center. Rainfall, temperature, humidity, and wind speed variables were collected. Functional Generalized Spectral Additive Model (FGSAM), Functional Generalized Linear Model (FGLM), and Functional Generalized Kernel Additive Model (FGKAM) were used to predict malaria incidence as a function of the pattern of meteorological indicators over a continuum of the 18 weeks preceding the week of interest. Their respective outcomes were compared in terms of predictive abilities. The results showed that (1) the highest malaria incidence rate occurred in the village 10 to 12 weeks after we observed a pattern of air humidity levels >65%, combined with two or more consecutive rain episodes and a mean wind speed <1.8 m/s; (2) among the three models, the FGLM obtained the best results in terms of prediction; and (3) FGSAM was shown to be a good compromise between FGLM and FGKAM in terms of flexibility and simplicity. The models showed that some meteorological conditions may provide a basis for detection of future outbreaks of malaria. The models developed in this paper are useful for implementing preventive strategies using past meteorological and past malaria incidence.

scholarly journals Hierarchical generalized additive models: an introduction with mgcv

2018 ◽  
Author(s):  
Eric J Pedersen ◽  
David L. Miller ◽  
Gavin L. Simpson ◽  
Noam Ross
Keyword(s):  
Hierarchical Model ◽  
Generalized Additive Model ◽  
Generalized Additive Models ◽  
Additive Model ◽  
Additive Models ◽  
Complex Structures ◽  
Ecological Data ◽  
Nonlinear Functional ◽  
Functional Relationships ◽  
Statistical Issues

In this paper, we discuss an extension to two popular approaches to modelling complex structures in ecological data: the generalized additive model (GAM) and the hierarchical model (HGLM). The hierarchical GAM (HGAM), allows modelling of nonlinear functional relationships between covariates and outcomes where the shape of the function itself varies between different grouping levels. We describe the theoretical connection between these models, HGLMs and GAMs, explain how to model different assumptions about the degree of inter-group variability in functional response, and show how HGAMs can be readily fitted using existing GAM software, the mgcv package in R. We also discuss computational and statistical issues with fitting these models, and demonstrate how to fit HGAMs on example data.

scholarly journals Estimating the impact of vaccination using age–time-dependent incidence rates of hepatitis B

2007 ◽  
Vol 136 (3) ◽  
pp. 341-351 ◽  
Author(s):  
N. HENS ◽  
M. AERTS ◽  
Z. SHKEDY ◽  
P. KUNG'U KIMANI ◽  
M. KOJOUHOROVA ◽  
...  
Keyword(s):  
Hepatitis B ◽  
Generalized Additive Models ◽  
Additive Model ◽  
Penalized Regression ◽  
Incidence Rates ◽  
Additive Models ◽  
Time Dependent ◽  
Model Framework ◽  
Penalized Regression Splines ◽  
The Impact

SUMMARYThe objective of this study was to model the age–time-dependent incidence of hepatitis B while estimating the impact of vaccination. While stochastic models/time-series have been used before to model hepatitis B cases in the absence of knowledge on the number of susceptibles, this paper proposed using a method that fits into the generalized additive model framework. Generalized additive models with penalized regression splines are used to exploit the underlying continuity of both age and time in a flexible non-parametric way. Based on a unique case notification dataset, we have shown that the implemented immunization programme in Bulgaria resulted in a significant decrease in incidence for infants in their first year of life with 82% (79–84%). Moreover, we have shown that conditional on an assumed baseline susceptibility percentage, a smooth force-of-infection profile can be obtained from which two local maxima were observed at ages 9 and 24 years.

scholarly journals Influence of meteorology on PM<sub>10</sub> trends and variability in Switzerland from 1991 to 2008

2011 ◽  
Vol 11 (4) ◽  
pp. 1813-1835 ◽  
Author(s):  
I. Barmpadimos ◽  
C. Hueglin ◽  
J. Keller ◽  
S. Henne ◽  
A. S. H. Prévôt
Keyword(s):  
Mean Squared Error ◽  
Generalized Additive Models ◽  
Additive Models ◽  
Weekend Effect ◽  
Meteorological Variables ◽  
Precipitation Rate ◽  
Wind Gust ◽  
Traffic Emission ◽  
Urban Street ◽  
Meteorological Observations

Abstract. Measurements of airborne particles with aerodynamic diameter of 10 μm or less (PM10) and meteorological observations are available from 13 stations distributed throughout Switzerland and representing different site types. The effect of all available meteorological variables on PM10 concentrations was estimated using Generalized Additive Models. Data from each season were treated separately. The most important variables affecting PM10 concentrations in winter, autumn and spring were wind gust, the precipitation rate of the previous day, the precipitation rate of the current day and the boundary layer depth. In summer, the most important variables were wind gust, Julian day and afternoon temperature. In addition, temperature was important in winter. A "weekend effect" was identified due to the selection of variable "day of the week" for some stations. Thursday contributes to an increase of 13% whereas Sunday contributes to a reduction of 12% of PM10 concentrations compared to Monday on average over 9 stations for the yearly data. The estimated effects of meteorological variables were removed from the measured PM10 values to obtain the PM10 variability and trends due to other factors and processes, mainly PM10 emissions and formation of secondary PM10 due to trace gas emissions. After applying this process, the PM10 variability was much lower, especially in winter where the ratio of adjusted over measured mean squared error was 0.27 on average over all considered sites. Moreover, PM10 trends in winter were more negative after the adjustment for meteorology and they ranged between −1.25 μg m−3 yr−1 and 0.07 μg m−3 yr−1. The adjusted trends for the other seasons ranged between −1.34 μg m−3 yr−1 and −0.26 μg m−3 yr−1 in spring, −1.40 μg m−3 yr−1 and −0.28 μg m−3 yr−1 in summer and −1.28 μg m−3 yr−1 and −0.11 μg m−3 yr−1 in autumn. The estimated trends of meteorologically adjusted PM10 were in general non-linear. The two urban street sites considered in the study, Bern and Lausanne, experienced the largest reduction in measured and adjusted PM10 concentrations. This indicates a verifiable effect of traffic emission reduction strategies implemented during the past two decades. The average adjusted yearly trends for rural, urban background and urban street stations were −0.37, −0.53 and −1.2 μg m−3 yr−1 respectively. The adjusted yearly trends for all stations range from −0.15 μg m−3 yr−1 to −1.2 μg m−3 yr−1 or −1.2% yr−1 to −3.3% yr−1.

scholarly journals Sensitivity of Precipitation Phase over the Swiss Alps to Different Meteorological Variables

2014 ◽  
Vol 15 (2) ◽  
pp. 685-696 ◽  
Author(s):  
S. Froidurot ◽  
I. Zin ◽  
B. Hingray ◽  
A. Gautheron
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Meteorological Data ◽  
Explanatory Power ◽  
Surface Air Temperature ◽  
Ground Level ◽  
Swiss Alps ◽  
Meteorological Variables ◽  
Study Region ◽  
Precipitation Phase

Abstract In most meteorological or hydrological models, the distinction between snow and rain is based only on a given air temperature. However, other factors such as air moisture can be used to better distinguish between the two phases. In this study, a number of models using different combinations of meteorological variables are tested to determine their pertinence for the discrimination of precipitation phases. Spatial robustness is also evaluated. Thirty years (1981–2010) of Swiss meteorological data are used, consisting of radio soundings from Payerne as well as present weather observations and surface measurements (mean hourly surface air temperature, mean hourly relative humidity, and hourly precipitation) from 14 stations, including Payerne. It appeared that, unlike surface variables, variables derived from the atmospheric profiles (e.g., the vertical temperature gradient) hardly improve the discrimination of precipitation phase at ground level. Among all tested variables, surface air temperature and relative humidity show the greatest explanatory power. The statistical model using these two variables and calibrated for the case study region provides good spatial robustness over the region. Its parameters appear to confirm those defined in the model presented by Koistinen and Saltikoff.

scholarly journals Significance between Air pollutants, Meteorological Factors and COVID-19 Infections: Probable Evidences in India

2020 ◽  
Author(s):  
Mrunmayee M Sahoo
Keyword(s):  
Relative Humidity ◽  
Air Pollutants ◽  
Meteorological Factors ◽  
Additive Model ◽  
Meteorological Factor ◽  
Health Concern ◽  
Generalised Additive Model ◽  
Unit Increase ◽  
Control And Prevention ◽  
Novel Coronavirus

Abstract SARS-CoV-2 (Coronavirus) disease represents the causative agent with a potentially fatal risk which is having great global human health concern. Earlier studies suggested that air pollutants and meteorological factors were considered as the risk factors for acute respiratory infection, which carries harmful pathogens and affects the immunity. The study intended to explore the correlation between air pollutants, meteorological factors and the daily reported infection cases caused by novel coronavirus in India. The daily positive infected cases, air pollution and meteorological factors in 288 districts were collected from January 30, 2020 to April 23, 2020 in India. Speraman’s correlation and generalised additive model were applied to investigate the correlations of four air pollutants (PM2.5, PM10, NO2 and SO2) and eight meteorological factors (Temp, DTR, RH, AH, AP, RF, WS and WD) with COVID-19 infected cases. The study indicated that a 10 µg/m3 increase during (Lag0-14) in PM2.5, PM10 and NO2 was resulted in 2.21% (95%CI: 1.13 to 3.29), 2.67% (95% CI: 0.33 to 5.01) and 4.56 (95% CI: 2.22 to 6.90) increase in daily counts of COVID 19 infected cases respectively. However, only 1 unit increase in meteorological factor levels in case of daily mean temperature and DTR during (Lag0-14) associated with 3.78% (95%CI: 1.81 to 5.75) and 1.82% (95% CI: -1.74 to 5.38) rise of COVID-19 infected cases respectively. In addition, SO2 and relative humidity were negatively associated with COVID-19 infected cases at Lag0-14 with decrease of 7.23% (95% CI: -10.99 to -3.47) and 1.11% (95% CI: -3.45 to 1.23) for SO2 and for relative humidity respectively. The study recommended that there is significant relationship between air pollutants and meteorological factors with COVID-19 infected cases, which substantially explain the effect of national lockdown and suggested positive implications for control and prevention of the spread of SARS-CoV-2 disease.

scholarly journals Challenges in the control of COVID-19 outbreaks caused by the delta variant during periods of low humidity: an observational study in Sydney, Australia

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Michael P. Ward ◽  
Yuanhua Liu ◽  
Shuang Xiao ◽  
Zhijie Zhang
Keyword(s):  
Time Series ◽  
Relative Humidity ◽  
Moving Average ◽  
Likelihood Ratio Statistic ◽  
Additive Model ◽  
Additive Models ◽  
Local Health ◽  
Weather Factors ◽  
Best Fitting ◽  
Sydney Australia

Abstract Background Since the appearance of severe acute respiratory coronavirus 2 (SARS-CoV-2) and the coronavirus disease 2019 (COVID-19) pandemic, a growing body of evidence has suggested that weather factors, particularly temperature and humidity, influence transmission. This relationship might differ for the recently emerged B.1.617.2 (delta) variant of SARS-CoV-2. Here we use data from an outbreak in Sydney, Australia that commenced in winter and time-series analysis to investigate the association between reported cases and temperature and relative humidity. Methods Between 16 June and 10 September 2021, the peak of the outbreak, there were 31,662 locally-acquired cases reported in five local health districts of Sydney, Australia. The associations between daily 9:00 am and 3:00 pm temperature (°C), relative humidity (%) and their difference, and a time series of reported daily cases were assessed using univariable and multivariable generalized additive models and a 14-day exponential moving average. Akaike information criterion (AIC) and the likelihood ratio statistic were used to compare different models and determine the best fitting model. A sensitivity analysis was performed by modifying the exponential moving average. Results During the 87-day time-series, relative humidity ranged widely (< 30–98%) and temperatures were mild (approximately 11–17 °C). The best-fitting (AIC: 1,119.64) generalized additive model included 14-day exponential moving averages of 9:00 am temperature (P < 0.001) and 9:00 am relative humidity (P < 0.001), and the interaction between these two weather variables (P < 0.001). Humidity was negatively associated with cases no matter whether temperature was high or low. The effect of lower relative humidity on increased cases was more pronounced below relative humidity of about 70%; below this threshold, not only were the effects of humidity pronounced but also the relationship between temperature and cases of the delta variant becomes apparent. Conclusions We suggest that the control of COVID-19 outbreaks, specifically those due to the delta variant, is particularly challenging during periods of the year with lower relative humidity and warmer temperatures. In addition to vaccination, stronger implementation of other interventions such as mask-wearing and social distancing might need to be considered during these higher risk periods. Graphical Abstract

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