1,122 research outputs found
A Review of 21st-Century Studies
PM10 prediction has attracted special legislative and scientific attention due
to its harmful effects on human health. Statistical techniques have the
potential for high-accuracy PM10 prediction and accordingly, previous studies
on statistical methods for temporal, spatial and spatio-temporal prediction of
PM10 are reviewed and discussed in this paper. A review of previous studies
demonstrates that Support Vector Machines, Artificial Neural Networks and
hybrid techniques show promise for suitable temporal PM10 prediction. A review
of the spatial predictions of PM10 shows that the LUR (Land Use Regression)
approach has been successfully utilized for spatial prediction of PM10 in
urban areas. Of the six introduced approaches for spatio-temporal prediction
of PM10, only one approach is suitable for high-resolved prediction (Spatial
resolution < 100 m; Temporal resolution ¤ 24 h). In this approach, based upon
the LUR modeling method, short-term dynamic input variables are employed as
explanatory variables alongside typical non-dynamic input variables in a non-
linear modeling procedure
Spatiotemporal modelling of PM concentrations in Lombardy (Italy) -- A comparative study
This study presents a comparative analysis of three predictive models with an
increasing degree of flexibility: hidden dynamic geostatistical models (HDGM),
generalised additive mixed models (GAMM), and the random forest spatiotemporal
kriging models (RFSTK). These models are evaluated for their effectiveness in
predicting PM concentrations in Lombardy (North Italy) from 2016 to
2020. Despite differing methodologies, all models demonstrate proficient
capture of spatiotemporal patterns within air pollution data with similar
out-of-sample performance. Furthermore, the study delves into station-specific
analyses, revealing variable model performance contingent on localised
conditions. Model interpretation, facilitated by parametric coefficient
analysis and partial dependence plots, unveils consistent associations between
predictor variables and PM concentrations. Despite nuanced variations
in modelling spatiotemporal correlations, all models effectively accounted for
the underlying dependence. In summary, this study underscores the efficacy of
conventional techniques in modelling correlated spatiotemporal data,
concurrently highlighting the complementary potential of Machine Learning and
classical statistical approaches
Identifying cloud droplets beyond lidar attenuation from vertically pointing cloud radar observations using artificial neural networks
In mixed-phase clouds, the variable mass ratio between liquid water and ice as well as the spatial distribution within the cloud plays an important role in cloud lifetime, precipitation processes, and the radiation budget. Data sets of vertically pointing Doppler cloud radars and lidars provide insights into cloud properties at high temporal and spatial resolution. Cloud radars are able to penetrate multiple liquid layers and can potentially be used to expand the identification of cloud phase to the entire vertical column beyond the lidar signal attenuation height, by exploiting morphological features in cloud radar Doppler spectra that relate to the existence of supercooled liquid. We present VOODOO (reVealing supercOOled liquiD beyOnd lidar attenuatiOn), a retrieval based on deep convolutional neural networks (CNNs) mapping radar Doppler spectra to the probability of the presence of cloud droplets (CD). The training of the CNN was realized using the Cloudnet processing suite as supervisor. Once trained, VOODOO yields the probability for CD directly at Cloudnet grid resolution. Long-term predictions of 18 months in total from two mid-latitudinal locations, i.e., Punta Arenas, Chile (53.1 S, 70.9 W), in the Southern Hemisphere and Leipzig, Germany (51.3 N, 12.4 E), in the Northern Hemisphere, are evaluated. Temporal and spatial agreement in cloud-droplet-bearing pixels is found for the Cloudnet classification to the VOODOO prediction. Two suitable case studies were selected, where stratiform, multi-layer, and deep mixed-phase clouds were observed. Performance analysis of VOODOO via classification-evaluating metrics reveals precision > 0.7, recall ≈ 0.7, and accuracy ≈ 0.8. Additionally, independent measurements of liquid water path (LWP) retrieved by a collocated microwave radiometer (MWR) are correlated to the adiabatic LWP, which is estimated using the temporal and spatial locations of cloud droplets from VOODOO and Cloudnet in connection with a cloud parcel model. This comparison resulted in stronger correlation for VOODOO (≈ 0.45) compared to Cloudnet (≈ 0.22) and indicates the availability of VOODOO to identify CD beyond lidar attenuation. Furthermore, the long-term statistics for 18 months of observations are presented, analyzing the performance as a function of MWR-LWP and confirming VOODOO's ability to identify cloud droplets reliably for clouds with LWP > 100 g m-2. The influence of turbulence on the predictive performance of VOODOO was also analyzed and found to be minor. A synergy of the novel approach VOODOO and Cloudnet would complement each other perfectly and is planned to be incorporated into the Cloudnet algorithm chain in the near future
A neural network-based scale-adaptive cloud-fraction scheme for GCMs
Cloud fraction significantly affects the short- and long-wave radiation. Its
realistic representation in general circulation models (GCMs) still poses great
challenges in modeling the atmosphere. Here, we present a neural network-based
diagnostic scheme that uses the grid-mean temperature, pressure, liquid and ice
water mixing ratios, and relative humidity to simulate the sub-grid cloud
fraction. The scheme, trained using CloudSat data with explicit consideration
of grid sizes, realistically simulates the observed cloud fraction with a
correlation coefficient (r) > 0.9 for liquid-, mixed-, and ice-phase clouds.
The scheme also captures the observed non-monotonic relationship between cloud
fraction and relative humidity and is computationally efficient, and robust for
GCMs with a variety of horizontal and vertical resolutions.
For illustrative purposes, we conducted comparative analyses of the 2006-2019
climatological-mean cloud fractions among CloudSat, and simulations from the
new scheme and the Xu-Randall scheme (optimized the same way as the new
scheme). The network-based scheme improves not only the spatial distribution of
the total cloud fraction but also the cloud vertical structure (r > 0.99). For
example, the biases of too-many high-level clouds over the tropics and too-many
low-level clouds over regions around 60{\deg}S and 60{\deg}N in the Xu-Randall
scheme are significantly reduced. These improvements are also found to be
insensitive to the spatio-temporal variability of large-scale meteorology
conditions, implying that the scheme can be used in different climate regimes
Urban air pollution modelling with machine learning using fixed and mobile sensors
Detailed air quality (AQ) information is crucial for sustainable urban management, and many regions in the world have built static AQ monitoring networks to provide AQ information. However, they can only monitor the region-level AQ conditions or sparse point-based air pollutant measurements, but cannot capture the urban dynamics with high-resolution spatio-temporal variations over the region. Without pollution details, citizens will not be able to make fully informed decisions when choosing their everyday outdoor routes or activities, and policy-makers can only make macroscopic regulating decisions on controlling pollution triggering factors and emission sources. An increasing research effort has been paid on mobile and ubiquitous sampling campaigns as they are deemed the more economically and operationally feasible methods to collect urban AQ data with high spatio-temporal resolution.
The current research proposes a Machine Learning based AQ Inference (Deep AQ) framework from data-driven perspective, consisting of data pre-processing, feature extraction and transformation, and pixelwise (grid-level) AQ inference. The Deep AQ framework is adaptable to integrate AQ measurements from the fixed monitoring sites (temporally dense but spatially sparse), and mobile low-cost sensors (temporally sparse but spatially dense). While instantaneous pollutant concentration varies in the micro-environment, this research samples representative values in each grid-cell-unit and achieves AQ inference at 1 km \times 1 km pixelwise scale. This research explores the predictive power of the Deep AQ framework based on samples from only 40 fixed monitoring sites in Chengdu, China (4,900 {\mathrm{km}}^\mathrm{2}, 26 April - 12 June 2019) and collaborative sampling from 28 fixed monitoring sites and 15 low-cost sensors equipped with taxis deployed in Beijing, China (3,025 {\mathrm{km}}^\mathrm{2}, 19 June - 16 July 2018).
The proposed Deep AQ framework is capable of producing high-resolution (1 km \times 1 km, hourly) pixelwise AQ inference based on multi-source AQ samples (fixed or mobile) and urban features (land use, population, traffic, and meteorological information, etc.). This research has achieved high-resolution (1 km \times 1 km, hourly) AQ inference (Chengdu: less than 1% spatio-temporal coverage; Beijing: less than 5% spatio-temporal coverage) with reasonable and satisfactory accuracy by the proposed methods in urban cases (Chengdu: SMAPE \mathrm{<} 20%; Beijing: SMAPE \mathrm{<} 15%). Detailed outcomes and main conclusions are provided in this thesis on the aspects of fixed and mobile sensing, spatio-temporal coverage and density, and the relative importance of urban features. Outcomes from this research facilitate to provide a scientific and detailed health impact assessment framework for exposure analysis and inform policy-makers with data driven evidence for sustainable urban management.Open Acces
Development of a Fast and Detailed Model of Urban-Scale Chemical and Physical Processing
Abstract and PDF report are also available on the MIT Joint Program on the Science and Policy of Global Change website (http://globalchange.mit.edu/).A reduced form metamodel has been produced to simulate the effects of physical, chemical, and meteorological processing of highly reactive trace species in hypothetical urban areas, which is capable of efficiently simulating the urban concentration, surface deposition, and net mass flux of these species. A polynomial chaos expansion and the probabilistic collocation method have been used for the metamodel, and its coefficients were fit so as to be applicable under a broad range of present-day and future conditions. The inputs upon which this metamodel have been formed are based on a combination of physical properties (average temperature, diurnal temperature range, date, and latitude), anthropogenic properties (patterns and amounts of emissions), and the surrounding environment (background concentrations of certain species).
Probability Distribution Functions (PDFs) of the inputs were used to run a detailed parent chemical and physical model, the Comprehensive Air Quality Model with Extensions (CAMx), thousands of times. Outputs from these runs were used in turn to both determine the coefficients of and test the precision of the metamodel, as compared with the detailed parent model. The deviations between the metamodel and the parent mode for many important species (O3, CO, NOx, and BC) were found to have a weighted RMS error less than 10% in all cases, with many of the specific cases having a weighted RMS error less than 1%. Some of the other important species (VOCs, PAN, OC, and sulfate aerosol) usually have their weighted RMS error less than 10% as well, except for a small number of cases. These cases, in which the highly non-linear nature of the processing is too large for the third order metamodel to give an accurate fit, are explained in terms of the complexity and non-linearity of the physical, chemical, and meteorological processing. In addition, for those species in which good fits have not been obtained, the program has been designed in such a way that values which are not physically realistic are flagged.
Sensitivity tests have been performed, to observe the response of the 16 metamodels (4 different meteorologies and 4 different urban types) to a broad set of potential inputs. These results were compared with observations of ozone, CO, formaldehyde, BC, and PM10 from a few well observed urban areas, and in most of the cases, the output distributions were found to be within ranges of the observations.
Overall, a set of efficient and robust metamodels have been generated which are capable of simulating the effects of various physical, chemical, and meteorological processing, and capable of determining the urban concentrations, mole fractions, and fluxes of species, important to human health and the climate.Federal Agencies and industries that sponsor the MIT Joint Program on the Science and Policy of Global Change
Spatio-temporal variation of throughfall in a hyrcanian plain forest stand in Northern Iran
Elucidating segregation of precipitation in different components in forest stands is important for proper forest ecosystems management. However, there is a lack of information on important rainfall components viz. throughfall, interception and stemflow in forest watersheds particularly in developing countries. We therefore investigated the spatiotemporal variation of important component of throughfall for a forest stand in a Hyrcanian plain forest in Noor City, northern Iran. The study area contained five species of Quercus castaneifolia, Carpinus betulus, Populus caspica and Parrotia persica. The research was conducted from July 2013 to July 2014 using a systematic sampling method. Ninetysix throughfall collectors were installed in a 3.5 m × 3.5 m grid cells. The canopy covers during the growing/leaf-on (i.e., from May to November) and non-growing/leaf-off (i.e., from December to March) seasons were approximately 41% and 81%, respectively. The mean cumulative throughfall during the study period was 623±31 mm. The average throughfall (TF) as % of rainfall (TFPR) during leaf-on and leaf-off periods were calculated 56±14% and 77±10%, respectively. TF was significantly (R2 = 0.97, p = 0.00006) correlated with gross precipitation. Percent of canopy cover was not correlated with TF except when gross precipitation was <30 mm. A comparison between leaf-off and leaf-on conditions indicated a significantly higher TFPR and corresponding hotspots during leaf-on period. TFPR also differed between seasons with a maximum amount in winter (82%). The results of the study can be effectively used by forest watershed managers for better perception of hydrological behavior of the Hyrcanian forest in the north of Iran under different silvicultural circumstances leading to getting better ecosystem services
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