NONLINEAR DETERMINISTIC ANALYSIS OF AIR POLLUTION DYNAMICS IN A RURAL AND AGRICULTURAL SETTING

2007 ◽  
Vol 10 (04) ◽  
pp. 581-597 ◽  
Author(s):  
BELLIE SIVAKUMAR ◽  
WESLEY W. WALLENDER ◽  
WILLIAM R. HORWATH ◽  
JEFFREY P. MITCHELL
Keyword(s):  
Air Pollution ◽  
Ozone Concentration ◽  
Nonlinear Dynamic ◽  
Urban Areas ◽  
Nearest Neighbor ◽  
Climate Station ◽  
Correlation Dimension Method ◽  
Low Dimensional ◽  
The University ◽  
Agricultural Setting

Applications of nonlinear dynamic tools for studying air pollution are gaining attention. Studies on ozone concentration in urban areas have reported the presence of low-dimensional deterministic natures and thus the possibility of good predictions of air pollution dynamics. In light of these encouraging results, a nonlinear deterministic approach is employed herein to study air pollution dynamics in a rural, and largely agricultural, setting in California. Specifically, air quality index (AQI) data observed at the University of California, Davis/National Oceanic and Atmospheric Administration (UCD/NOAA) climate station are studied. Four different daily AQI types of data are analyzed: maximum, minimum, difference (between maximum and minimum), and average. The correlation dimension method, a nonlinear dynamic technique that uses phase–space reconstruction and nearest neighbor concepts, is employed to identify the nature of the underlying dynamics, whether high-dimensional or low-dimensional. Correlation dimensions of 5.12, 6.20, 6.68, and 5.84 obtained for the above four series, respectively, indicate the presence of low-dimensional deterministic behavior, with six or seven dominant governing variables in the underlying dynamics. The dimension results and number of variables are in reasonable agreement with those reported by past studies, even though the studied data are different: rural versus urban, and AQI versus ozone concentration. Future efforts will focus on strengthening the present results on the nature of air pollution dynamics, identifying the actual governing variables, and predictions of air pollution dynamics.

Finding the right tree for future urban planning – Meso- to microscale model coupling in urban areas

2020 ◽  
Author(s):  
Joachim Fallmann ◽  
Helge Simon ◽  
Tim Sinsel ◽  
Marc Barra ◽  
Holger Tost
Keyword(s):  
Air Pollution ◽  
Urban Planning ◽  
Urban Area ◽  
Ozone Concentration ◽  
Tree Species ◽  
Urban Areas ◽  
Street Canyon ◽  
Model Systems ◽  
Traffic Load ◽  
Microscale Model

<p><span>It has been long understood that green infrastructure helps to mitigate urban heat island formation and therefore should be a key strategy in future urban planning practices. Due to its high level of heat resilience, the sycamore tree (Platanus) dominates the appearance of urban landscapes in central Europe. Under extreme climate conditions however, these species tend to emit high levels of biogenic volatile organic compounds (BVOCs) which in turn can act as precursors for tropospheric ozone, especially in highly NOx polluted environments such as urban areas. </span></p><p><span>Assessing the ozone air quality of a large urban area in Germany we use the state-of-the art regional chemical transport model MECO(n), with chemistry coming from the Modular Earth Submodel System (MESSy) and meteorology being calculated by COSMO. Including the latest version of TERRA_URB, the model is configured for the Rhine-Main urban area. In a second step, we implement parts of the regional atmospheric chemistry mechanism in the ENVI-met model framework in order to investigate the impact of isoprene emissions on ozone concentration at street level for the urban area of Mainz, Germany. </span></p><p><span>Whereas mesoscale model results only show moderate mean ozone pollution over the model area, at micro-scale level on selected hot spots we find a clear relationship between urban layout, proximity to NOx emitters, tree-species-dependent isoprene emission capacity and increase in ozone concentration. The ENVI-met study reveals, that next to tree species, its location is a key factor for its micro-climatic UHI and air pollution mitigation potential. We could show, that isoprene related ozone concentration is highly sensitive to leaf temperature, photosynthetic active radiation as well as to the proximity to NO2 pollution sources. In a street canyon with high traffic load we find significant correlations between diurnal boundary layer dynamics, morning and evening rush hour and ambient ozone levels. For a hot summer day in particular, we simulate ozone concentrations rising up to 500% within a weakly ventilated street canyon with a high amount of strong isoprene emitters being present.</span></p><p><span>We summarize that combining findings from meso- and microscale model systems can be an important asset for science tools for cities in the framework of climate change adaption and mitigation </span><span>and air pollution abatement</span><span> strategies.</span></p>

scholarly journals Hydrologic system complexity and nonlinear dynamic concepts for a catchment classification framework

2012 ◽  
Vol 16 (11) ◽  
pp. 4119-4131 ◽  
Author(s):  
B. Sivakumar ◽  
V. P. Singh
Keyword(s):  
Time Series ◽  
Nonlinear Dynamic ◽  
Nearest Neighbor ◽  
Large Network ◽  
Modeling Framework ◽  
System Complexity ◽  
Classification Framework ◽  
Monthly Streamflow ◽  
Hydrologic System ◽  
Correlation Dimension Method

Abstract. The absence of a generic modeling framework in hydrology has long been recognized. With our current practice of developing more and more complex models for specific individual situations, there is an increasing emphasis and urgency on this issue. There have been some attempts to provide guidelines for a catchment classification framework, but research in this area is still in a state of infancy. To move forward on this classification framework, identification of an appropriate basis and development of a suitable methodology for its representation are vital. The present study argues that hydrologic system complexity is an appropriate basis for this classification framework and nonlinear dynamic concepts constitute a suitable methodology. The study employs a popular nonlinear dynamic method for identification of the level of complexity of streamflow and for its classification. The correlation dimension method, which has its base on data reconstruction and nearest neighbor concepts, is applied to monthly streamflow time series from a large network of 117 gaging stations across 11 states in the western United States (US). The dimensionality of the time series forms the basis for identification of system complexity and, accordingly, streamflows are classified into four major categories: low-dimensional, medium-dimensional, high-dimensional, and unidentifiable. The dimension estimates show some "homogeneity" in flow complexity within certain regions of the western US, but there are also strong exceptions.

An Urban Air Pollution Early Warning System Based on PM2.5 Prediction Applied in Ploiesti City

2017 ◽  
Vol 68 (4) ◽  
pp. 858-863
Author(s):  
Mihaela Oprea ◽  
Marius Olteanu ◽  
Radu Teodor Ianache
Keyword(s):  
Air Pollution ◽  
Early Warning ◽  
Urban Areas ◽  
Early Warning System ◽  
Urban Air Pollution ◽  
Warning System ◽  
Air Pollutant ◽  
Pollution Level ◽  
Urban Air ◽  
Prediction Approach

Fine particulate matter with a diameter less than 2.5 �m (i.e. PM2.5) is an air pollutant of special concern for urban areas due to its potential significant negative effects on human health, especially on children and elderly people. In order to reduce these effects, new tools based on PM2.5 monitoring infrastructures tailored to specific urban regions are needed by the local and regional environmental management systems for the provision of an expert support to decision makers in air quality planning for cities and also, to inform in real time the vulnerable population when PM2.5 related air pollution episodes occur. The paper focuses on urban air pollution early warning based on PM2.5 prediction. It describes the methodology used, the prediction approach, and the experimental system developed under the ROKIDAIR project for the analysis of PM2.5 air pollution level, health impact assessment and early warning of sensitive people in the Ploiesti city. The PM2.5 concentration evolution prediction is correlated with PM2.5 air pollution and health effects analysis, and the final result is processed by the ROKIDAIR Early Warning System (EWS) and sent as a message to the affected population via email or SMS. ROKIDAIR EWS is included in the ROKIDAIR decision support system.

Improving the Protection of Children Against Air Pollution Threats in Romania � The RokidAIR Project Approach and Future Perspectives

2017 ◽  
Vol 68 (4) ◽  
pp. 841-846
Author(s):  
Hai-Ying Liu ◽  
Daniel Dunea ◽  
Mihaela Oprea ◽  
Tom Savu ◽  
Stefania Iordache
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Health Effects ◽  
Urban Areas ◽  
Behavioural Change ◽  
Health Assessment ◽  
Warning System ◽  
Personal Exposure ◽  
Mobile Technologies ◽  
Quality Maps

This paper presents the approach used to develop the information chain required to reach the objectives of the EEA Grants� RokidAIR project in two Romanian cities i.e., Targoviste and Ploiesti. It describes the PM2.5 monitoring infrastructure and architecture to the web-based GIS platform, the early warning system and the decision support system, and finally, the linking of air pollution to health effects in children. In addition, it shows the analysis performance of the designed system to process the collected time series from various data sources using the benzene concentrations monitored in Ploiesti. Moreover, this paper suggests that biomarkers, mobile technologies, and Citizens� Observatories are potential perspectives to improve data coverage by the provision of near-real-time air quality maps, and provide personal exposure and health assessment results, enabling the citizens� engagement and behavioural change. This paper also addresses new fields in nature-based solutions to improve air quality, and studies on air pollution and its mental health effects in the urban areas of Romania.

Hydroponic Nutrient Control System Based on Internet of Things

2019 ◽  
Vol 13 (2) ◽  
Author(s):  
Herman Herman ◽  
Demi Adidrana ◽  
Nico Surantha ◽  
Suharjito Suharjito
Keyword(s):  
Urban Areas ◽  
Human Population ◽  
Mineral Water ◽  
Nearest Neighbor ◽  
Total Dissolved Solids ◽  
K Nearest Neighbor ◽  
Turn On ◽  
Nutrient Film Technique ◽  
Planting Method

The human population significantly increases in crowded urban areas. It causes a reduction of available farming land. Therefore, a landless planting method is needed to supply the food for society. Hydroponics is one of the solutions for gardening methods without using soil. It uses nutrient-enriched mineral water as a nutrition solution for plant growth. Traditionally, hydroponic farming is conducted manually by monitoring the nutrition such as acidity or basicity (pH), the value of Total Dissolved Solids (TDS), Electrical Conductivity (EC), and nutrient temperature. In this research, the researchers propose a system that measures pH, TDS, and nutrient temperature values in the Nutrient Film Technique (NFT) technique using a couple of sensors. The researchers use lettuce as an object of experiment and apply the k-Nearest Neighbor (k-NN) algorithm to predict the classification of nutrient conditions. The result of prediction is used to provide a command to the microcontroller to turn on or off the nutrition controller actuators simultaneously at a time. The experiment result shows that the proposed k-NN algorithm achieves 93.3% accuracy when it is k = 5.

scholarly journals Development of Land Use Regression Model for Seasonal Variation of Nitrogen Dioxide (NO2) in Lahore, Pakistan

2021 ◽  
Vol 13 (9) ◽  
pp. 4933
Author(s):  
Saimar Pervez ◽  
Ryuta Maruyama ◽  
Ayesha Riaz ◽  
Satoshi Nakai
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Regression Model ◽  
Urban Areas ◽  
Industrial Development ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Land Use Classification ◽  
Land Use Regression ◽  
Air Exposure

Ambient air pollution and its exposure has been a worldwide issue and can increase the possibility of health risks especially in urban areas of developing countries having the mixture of different air pollution sources. With the increase in population, industrial development and economic prosperity, air pollution is one of the biggest concerns in Pakistan after the occurrence of recent smog episodes. The purpose of this study was to develop a land use regression (LUR) model to provide a better understanding of air exposure and to depict the spatial patterns of air pollutants within the city. Land use regression model was developed for Lahore city, Pakistan using the average seasonal concentration of NO2 and considering 22 potential predictor variables including road network, land use classification and local specific variable. Adjusted explained variance of the LUR models was highest for post-monsoon (77%), followed by monsoon (71%) and was lowest for pre-monsoon (70%). This is the first study conducted in Pakistan to explore the applicability of LUR model and hence will offer the application in other cities. The results of this study would also provide help in promoting epidemiological research in future.

scholarly journals Development of a Line Source Dispersion Model for Gaseous Pollutants by Incorporating Wind Shear near the Ground under Stable Atmospheric Conditions

2020 ◽  
Vol 4 (1) ◽  
pp. 17
Author(s):  
Saisantosh Vamshi Harsha Madiraju ◽  
Ashok Kumar
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Wind Shear ◽  
Dispersion Model ◽  
Near Field ◽  
Ground Level ◽  
Gaseous Pollutants ◽  
Air Pollution Control ◽  
Mobile Sources ◽  
Input Variables

Transportation sources are a major contributor to air pollution in urban areas. The role of air quality modeling is vital in the formulation of air pollution control and management strategies. Many models have appeared in the literature to estimate near-field ground level concentrations from mobile sources moving on a highway. However, current models do not account explicitly for the effect of wind shear (magnitude) near the ground while computing the ground level concentrations near highways from mobile sources. This study presents an analytical model based on the solution of the convective-diffusion equation by incorporating the wind shear near the ground for gaseous pollutants. The model input includes emission rate, wind speed, wind direction, turbulence, and terrain features. The dispersion coefficients are based on the near field parameterization. The sensitivity of the model to compute ground level concentrations for different inputs is presented for three different downwind distances. In general, the model shows Type III sensitivity (i.e., the errors in the input will show a corresponding change in the computed ground level concentrations) for most of the input variables. However, the model equations should be re-examined for three input variables (wind velocity at the reference height and two variables related to the vertical spread of the plume) to make sure that that the model is valid for computing ground level concentrations.

scholarly journals Morpho-Physio-Biochemical Attributes of Roadside Trees as Potential Tools for Biomonitoring of Air Quality and Environmental Health in Urban Areas

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 236
Author(s):  
Ha Na You ◽  
Myeong Ja Kwak ◽  
Sun Mi Je ◽  
Jong Kyu Lee ◽  
Yea Ji Lim ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Environmental Health ◽  
Urban Areas ◽  
Biochemical Parameters ◽  
Lipid Peroxide ◽  
Platanus Occidentalis ◽  
Physiological Variables ◽  
Sources Of Pollution ◽  
Species Specific

Environmental pollution is an important issue in metropolitan areas, and roadside trees are directly affected by various sources of pollution to which they exhibit numerous responses. The aim of the present study was to identify morpho-physio-biochemical attributes of maidenhair tree (Ginkgo biloba L.) and American sycamore (Platanus occidentalis L.) growing under two different air quality conditions (roadside with high air pollution, RH and roadside with low air pollution, RL) and to assess the possibility of using their physiological and biochemical parameters as biomonitoring tools in urban areas. The results showed that the photosynthetic rate, photosynthetic nitrogen-use efficiencies, and photochromic contents were generally low in RH in both G. biloba and P. occidentalis. However, water-use efficiency and leaf temperature showed high values in RH trees. Among biochemical parameters, in G. biloba, the lipid peroxide content was higher in RH than in RL trees, but in P. occidentalis, this content was lower in RH than in RL trees. In both species, physiological activities were low in trees planted in areas with high levels of air pollution, whereas their biochemical and morphological variables showed different responses to air pollution. Thus, we concluded that it is possible to determine species-specific physiological variables affected by regional differences of air pollution in urban areas, and these findings may be helpful for monitoring air quality and environmental health using trees.

scholarly journals Ozone effect on inflammatory and proteomic profile of human macrophages and airway epithelial cells

2020 ◽  
Vol 30 (Supplement_5) ◽  
Author(s):  
L Falcone ◽  
E Aruffo ◽  
P Di Carlo ◽  
P Del Boccio ◽  
M C Cufaro ◽  
...  
Keyword(s):  
Oxidative Stress ◽  
Air Pollution ◽  
Epithelial Cells ◽  
Cell Lines ◽  
Urban Areas ◽  
Airway Epithelial Cells ◽  
Airway Epithelial ◽  
Proteomics Analysis ◽  
Human Macrophages ◽  
Tnf Α

Abstract Background Reactive oxygen species (ROS) and oxidative stress in the respiratory system are involved in lung inflammation and tumorigenesis. Ozone (O3) is one of the main components of air pollution in urban areas able to act as strong pro-oxidant agent, however its effects on human health is still poorly investigated. In this study the effect of O3 has been evaluated in THP-1 monocytes differentiated into macrophages with PMA and in HBEpC (primary human bronchial epithelial) cells, two model systems for in vitro studies and translational research. Methods Cell viability, ROS and pro-inflammatory cytokines like interleukin-8(IL-8) and tumor necrosis factor(TNF-α) have been tested in the above-mentioned cell lines not exposed to any kind of pollution (basal condition-b.c.) or exposed to O3 at a concentration of 120 ppb. In HBEpC a labelfree shotgun proteomics analysis has been also performed in the same conditions. Results Ozone significantly increased the production of IL-8 and TNF-α in THP-1 whereas no changes were shown in HBEpC. In both cell lines lipopolysaccharide(LPS) caused an increase of IL-8 and TNF-α production in b.c. and O3 treatment potentiated this effect. Ozone exposure increased ROS formation in a time dependent manner in both cell lines and in THP-1 cells a decrease in catalase activity was also shown. Finally, according to these data, functional proteomics analysis revealed that in HBEpC exposure to O3 many differential proteins are related to oxidative stress and inflammation. Conclusions Our results indicate that O3, at levels that can be reached in urban areas, causes an increase of pro-inflammatory agents either per se or potentiating the effect of immune response stimulators in cell models of human macrophages and human airway epithelial cells. Interestingly, the proteomic analysis showed that besides the dysregulated proteins, O3 induced the expression of AKR1D1 and AKR1B10, proteins recognized to play a significant role in cancer development. Key messages This study adds new pieces of information on the association between O3 exposure and detrimental effects on respiratory system. This study suggests the need for further research on the mechanisms involved and for a continued monitoring/re-evaluation of air pollution standards aimed at safeguarding human health.

Sign in / Sign up

Export Citation Format

Share Document