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Journal articles on the topic 'Regional scale'

To see the other types of publications on this topic, follow the link: Regional scale.

Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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1

Klima, David A., Sarah H. Seiler, Jeff B. Peterson, A. Britton Christmas, John M. Green, Greg Fleming, Michael H. Thomason, and Ronald F. Sing. "Full-scale regional exercises." Journal of Trauma and Acute Care Surgery 73, no. 3 (September 2012): 592–98. http://dx.doi.org/10.1097/ta.0b013e318265cbb2.

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2

Rockel, Burkhardt, Raymond Arritt, Markku Rummukainen, and Andreas Hense. "The 2nd Lund Regional-scale Climate Modelling Workshop." Meteorologische Zeitschrift 19, no. 4 (August 1, 2010): 323–24. http://dx.doi.org/10.1127/0941-2948/2010/0462.

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3

ZIKU, Humihiko. "Regional small-scale sewage treatment." Journal of Environmental Conservation Engineering 15, no. 5 (1986): 375–79. http://dx.doi.org/10.5956/jriet.15.375.

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4

Biosca, Oriol, Klaus Spiekermann, and Marcin Stępniak. "Transport accessibility at regional scale." Europa XXI 24 (2013): 5–17. http://dx.doi.org/10.7163/eu21.2013.24.1.

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5

Chand, Annisa. "Nitrogen boundaries at regional scale." Nature Food 3, no. 11 (November 16, 2022): 888. http://dx.doi.org/10.1038/s43016-022-00652-2.

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6

Gioli, B., and F. Miglietta. "Carbon balance assessment at regional scale." Forest@ - Rivista di Selvicoltura ed Ecologia Forestale 4, no. 4 (December 20, 2007): 469–77. http://dx.doi.org/10.3832/efor0486-0040469.

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7

Lowenthal, Douglas H., John G. Watson, Darko Koracin, L. W. Antony Chen, David Dubois, Ramesh Vellore, Naresh Kumar, et al. "Evaluation of Regional-Scale Receptor Modeling." Journal of the Air & Waste Management Association 60, no. 1 (January 2010): 26–42. http://dx.doi.org/10.3155/1047-3289.60.1.26.

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8

Cuthrie, John A. "ECONOMIES OF SCALE AND REGIONAL DEVELOPMENT." Papers in Regional Science 1, no. 1 (January 14, 2005): 121–31. http://dx.doi.org/10.1111/j.1435-5597.1955.tb01423.x.

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9

Toon, O. B., A. Robock, R. P. Turco, C. Bardeen, L. Oman, and G. L. Stenchikov. "Consequences of Regional-Scale Nuclear Conflicts." Science 315, no. 5816 (March 2, 2007): 1224–25. http://dx.doi.org/10.1126/science.1137747.

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10

Vallega, Adalberto. "The regional scale of ocean management." Ocean & Coastal Management 39, no. 3 (July 1998): 179–88. http://dx.doi.org/10.1016/s0964-5691(98)00028-3.

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11

de la Riva, Juan, Fernando Pérez-Cabello, Noemí Lana-Renault, and Nikos Koutsias. "Mapping wildfire occurrence at regional scale." Remote Sensing of Environment 92, no. 2 (August 2004): 288–94. http://dx.doi.org/10.1016/j.rse.2004.06.013.

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12

de la Riva, Juan, Fernando Pérez-Cabello, Noemí Lana-Renault, and Nikos Koutsias. "Mapping wildfire occurrence at regional scale." Remote Sensing of Environment 92, no. 3 (August 2004): 363–69. http://dx.doi.org/10.1016/j.rse.2004.06.022.

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13

Wolfe, Amy K., Nichole Kerchner, and Tom Wilbanks. "Public involvement on a regional scale." Environmental Impact Assessment Review 21, no. 5 (September 2001): 431–48. http://dx.doi.org/10.1016/s0195-9255(01)00084-1.

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14

Zwiers, Francis W., and Xuebin Zhang. "Toward Regional-Scale Climate Change Detection." Journal of Climate 16, no. 5 (March 2003): 793–97. http://dx.doi.org/10.1175/1520-0442(2003)016<0793:trsccd>2.0.co;2.

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15

Arritt, Raymond W., and Markku Rummukainen. "Challenges in Regional-Scale Climate Modeling." Bulletin of the American Meteorological Society 92, no. 3 (March 1, 2011): 365–68. http://dx.doi.org/10.1175/2010bams2971.1.

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16

O'Neill, R. V., C. T. Hunsaker, S. P. Timmins, B. L. Jackson, K. B. Jones, K. H. Riitters, and J. D. Wickham. "Scale problems in reporting landscape pattern at the regional scale." Landscape Ecology 11, no. 3 (June 1996): 169–80. http://dx.doi.org/10.1007/bf02447515.

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17

Robertson, H. Thomas, and Michael P. Hlastala. "Microsphere maps of regional blood flow and regional ventilation." Journal of Applied Physiology 102, no. 3 (March 2007): 1265–72. http://dx.doi.org/10.1152/japplphysiol.00756.2006.

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Systematically mapped samples cut from lungs previously labeled with intravascular and aerosol microspheres can be used to create high-resolution maps of regional perfusion and regional ventilation. With multiple radioactive or fluorescent microsphere labels available, this methodology can compare regional flow responses to different interventions without partial volume effects or registration errors that complicate interpretation of in vivo imaging measurements. Microsphere blood flow maps examined at different levels of spatial resolution have revealed that regional flow heterogeneity increases progressively down to an acinar level of scale. This pattern of scale-dependent heterogeneity is characteristic of a fractal distribution network, and it suggests that the anatomic configuration of the pulmonary vascular tree is the primary determinant of high-resolution regional flow heterogeneity. At ∼2-cm3 resolution, the large-scale gravitational gradients of blood flow per unit weight of alveolar tissue account for <5% of the overall flow heterogeneity. Furthermore, regional blood flow per gram of alveolar tissue remains relatively constant with different body positions, gravitational stresses, and exercise. Regional alveolar ventilation is accurately represented by the deposition of inhaled 1.0-μm fluorescent microsphere aerosols, at least down to the ∼2-cm3 level of scale. Analysis of these ventilation maps has revealed the same scale-dependent property of regional alveolar ventilation heterogeneity, with a strong correlation between ventilation and blood flow maintained at all levels of scale. The ventilation-perfusion (V̇a/Q̇) distributions obtained from microsphere flow maps of normal animals agree with simultaneously acquired multiple inert-gas elimination technique V̇a/Q̇ distributions, but they underestimate gas-exchange impairment in diffuse lung injury.

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18

Kubo, Yuji. "SCALE ECONOMIES, REGIONAL EXTERNALITIES, AND THE POSSIBILITY OF UNEVEN REGIONAL DEVELOPMENT*." Journal of Regional Science 35, no. 1 (February 1995): 29–42. http://dx.doi.org/10.1111/j.1467-9787.1995.tb01398.x.

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19

Bou-Zeid, Elie, Marc B. Parlange, and Charles Meneveau. "On the Parameterization of Surface Roughness at Regional Scales." Journal of the Atmospheric Sciences 64, no. 1 (January 1, 2007): 216–27. http://dx.doi.org/10.1175/jas3826.1.

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Abstract A parameterization for surface roughness and blending height at regional scales, under neutral atmospheric stability, is studied and tested. The analysis is based on a suite of large-eddy simulations (LES) over surfaces with varying roughness height and multiple variability scales. The LES are based on the scale-dependent Lagrangian dynamic subgrid-scale model, and the surface roughnesses at the ground are imposed using the rough-wall logarithmic law. Several patterns of roughness distribution are considered, including random tiling of patches with a wide distribution of length scales. An integral length scale, based on the one-dimensional structure function of the spatially variable roughness height, is used to define the characteristic surface variability scale, which is a critical input in many regional parameterization schemes. Properties of the simulated flow are discussed with special emphasis on the turbulence properties over patches of unequal roughness. The simulations are then used to assess a generalized form of the parameterization for the blending height and the equivalent surface roughness at regional scales that has been developed earlier for regular patterns of surface roughness (regular stripes). The results are also compared with other parameterizations proposed in the literature. Good agreement is found between the simulations and the regional-scale parameterization for the surface roughness and the blending height when this parameterization is combined with the characteristic surface variability scale proposed in this paper.

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20

Yu, Tao, Qiang Zhang, and Rui Sun. "Comparison of Machine Learning Methods to Up-Scale Gross Primary Production." Remote Sensing 13, no. 13 (June 23, 2021): 2448. http://dx.doi.org/10.3390/rs13132448.

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Eddy covariance observation is an applicable way to obtain accurate and continuous carbon flux at flux tower sites, while remote sensing technology could estimate carbon exchange and carbon storage at regional and global scales effectively. However, it is still challenging to up-scale the field-observed carbon flux to a regional scale, due to the heterogeneity and the unstable air conditions at the land surface. In this paper, gross primary production (GPP) from ground eddy covariance systems were up-scaled to a regional scale by using five machine learning methods (Cubist regression tree, random forest, support vector machine, artificial neural network, and deep belief network). Then, the up-scaled GPP were validated using GPP at flux tower sites, weighted GPP in the footprint, and MODIS GPP products. At last, the sensitivity of the input data (normalized difference vegetation index, fractional vegetation cover, shortwave radiation, relative humidity and air temperature) to the precision of up-scaled GPP was analyzed, and the uncertainty of the machine learning methods was discussed. The results of this paper indicated that machine learning methods had a great potential in up-scaling GPP at flux tower sites. The validation of up-scaled GPP, using five machine learning methods, demonstrated that up-scaled GPP using random forest obtained the highest accuracy.

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21

Wang, Binyan, Junfeng Tian, Peifeng Yang, and Baojie He. "Multi-Scale Features of Regional Poverty and the Impact of Geographic Capital: A Case Study of Yanbian Korean Autonomous Prefecture in Jilin Province, China." Land 10, no. 12 (December 20, 2021): 1406. http://dx.doi.org/10.3390/land10121406.

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Poverty is a challenge worldwide. Policy and regulations guiding anti-poverty measures for governments, NGOs, and multilateral institutions have not considered the spatial scale effect of regional poverty, resulting in low-efficiency poverty alleviation actions. This study addressed research gaps by analyzing the multi-scale (county, township, and village) features of regional poverty in Yanbian Korean Autonomous Prefecture in Jilin province, China. It examined the impact of geographic capital and associated spatial heterogeneity from four dimensions: natural environment, transport location, facilities accessibility, and socioeconomic development. The results identified that regional poverty varied at different scales: lower-scale poverty had higher levels of spatial differences, agglomeration, and spatial autocorrelation than higher-scale poverty, and the “island effect” was prominent. The factors potentially impacting regional poverty varied at different scales for geographical capital. At the township scale, only transport location and socioeconomic development dimensions could make significant differences. Factors in all four dimensions could affect village-scale poverty significantly, and the natural environment dimension was more effective than the other three dimensions. The impact of geographic capital and its spatial heterogeneity at the village scale varied, implying that local and diverse anti-poverty measures should increase. This study improves understanding of the multi-scale features of regional poverty and supports the formulation of effective anti-poverty measures.

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22

Hawkins, Seth C., Henderson McGinnis, and Philip Visser. "Organizing Wilderness Medicine on a Regional Scale." Wilderness and Environmental Medicine 19, no. 4 (2008): 305. http://dx.doi.org/10.1580/1080-6032-19.4.305.

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23

Graham, R. L., C. T. Hunsaker, R. V. O'Neill, and B. L. Jackson. "Ecological Risk Assessment at The Regional Scale." Ecological Applications 1, no. 2 (May 1991): 196–206. http://dx.doi.org/10.2307/1941812.

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24

Bashkin, V. N. "Geopolitical Risks: from Local to Regional Scale." Issues of Risk Analysis 17, no. 6 (December 29, 2020): 8–9. http://dx.doi.org/10.32686/1812-5220-2020-17-6-8-9.

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25

UNO, Itsushi, Toshimasa OHARA, and Shinsuke SATAKE. "Regional Scale Numerical Simulation of Yellow Sand." Wind Engineers, JAWE 2002, no. 91 (2002): 49–54. http://dx.doi.org/10.5359/jawe.2002.91_49.

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26

Gopalakrishnan, Ganesh, and Bruce Cornuelle. "Palau’s Effects on Regional-Scale Ocean Circulation." Oceanography 32, no. 4 (December 1, 2019): 126–35. http://dx.doi.org/10.5670/oceanog.2019.418.

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27

Johns, Tim, J. Iwan Jones, Lee Knight, Louise Maurice, Paul Wood, and Anne Robertson. "Regional-scale drivers of groundwater faunal distributions." Freshwater Science 34, no. 1 (March 2015): 316–28. http://dx.doi.org/10.1086/678460.

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28

&NA;. "A Proposed Regional Anesthesia Motor Scale (RAMS)." Regional Anesthesia and Pain Medicine 23, no. 5 (September 1998): 524–25. http://dx.doi.org/10.1097/00115550-199823050-00022.

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29

Kuo, Hung-Chi, and R. T. Williams. "Scale-Dependent Accuracy in Regional Spectral Methods." Monthly Weather Review 126, no. 10 (October 1998): 2640–47. http://dx.doi.org/10.1175/1520-0493(1998)126<2640:sdairs>2.0.co;2.

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30

Fabbricino, Massimiliano. "Modelling industrial waste management at regional scale." International Journal of Environment and Waste Management 7, no. 3/4 (2011): 279. http://dx.doi.org/10.1504/ijewm.2011.039469.

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31

PUE, A. "A proposed regional anesthesia motor scale (RAMS)." Regional Anesthesia and Pain Medicine 23, no. 5 (September 1998): 524–25. http://dx.doi.org/10.1016/s1098-7339(98)90044-x.

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32

Tulet, P., V. Crassier, and R. Rosset. "Air pollution modelling at a regional scale." Environmental Modelling & Software 15, no. 6-7 (September 2000): 693–701. http://dx.doi.org/10.1016/s1364-8152(00)00039-6.

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33

Lamon, E. Conrad, and Craig A. Stow. "Bayesian methods for regional-scale eutrophication models." Water Research 38, no. 11 (June 2004): 2764–74. http://dx.doi.org/10.1016/j.watres.2004.03.019.

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34

MOILANEN, ATTE, BRENDAN A. WINTLE, JANE ELITH, and MARK BURGMAN. "Uncertainty Analysis for Regional-Scale Reserve Selection." Conservation Biology 20, no. 6 (December 2006): 1688–97. http://dx.doi.org/10.1111/j.1523-1739.2006.00560.x.

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35

Hunsaker, Carolyn T., Robin L. Graham, Glenn W. Suter, Robert V. O'Neill, Lawrence W. Barnthouse, and Robert H. Gardner. "Assessing ecological risk on a regional scale." Environmental Management 14, no. 3 (May 1990): 325–32. http://dx.doi.org/10.1007/bf02394200.

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36

Annetts, David, and Juerg Hauser. "Target detectability in regional-scale AEM surveys." ASEG Extended Abstracts 2019, no. 1 (November 11, 2019): 1–6. http://dx.doi.org/10.1080/22020586.2019.12073021.

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37

Demetriades, Alecos, Manfred Birke, Stefano Albanese, Ilse Schoeters, and Benedetto De Vivo. "Continental, regional and local scale geochemical mapping." Journal of Geochemical Exploration 154 (July 2015): 1–5. http://dx.doi.org/10.1016/j.gexplo.2015.02.011.

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38

Jakobson, Anatol, and Ivan D. Blinov. "Multi-Scale Approach and “Playing Scales” in Economic Geography." Studies of the Industrial Geography Commission of the Polish Geographical Society 27 (January 3, 2014): 211–18. http://dx.doi.org/10.24917/20801653.27.13.

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The notions of multiscaleness and “playing scales” are considered for various spheres of economic geography: cartography, settlement geography, toponymics, ethno-historical geography, locating industrial production, touristic promotion, micro-regional (intra-urban) policy. The paper presents the idea of multiscaleness and “playing scales”, as well as the possibility of application of a multi- scale approach in economic geography.

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39

Tutangata, Tamari’i, and Mary Power. "The regional scale of ocean governance regional cooperation in the Pacific Islands." Ocean & Coastal Management 45, no. 11-12 (January 2002): 873–84. http://dx.doi.org/10.1016/s0964-5691(02)00111-4.

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40

Honorio Coronado, E. N., T. R. Baker, O. L. Phillips, N. C. A. Pitman, R. T. Pennington, R. Vásquez Martínez, A. Monteagudo, et al. "Integrating regional and continental scale comparisons of tree composition in Amazonian terra firme forests." Biogeosciences Discussions 6, no. 1 (January 29, 2009): 1421–51. http://dx.doi.org/10.5194/bgd-6-1421-2009.

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Abstract. We contrast regional and continental-scale comparisons of the floristic composition of terra firme forest in South Amazonia, using 55 plots across Amazonia and a subset of 30 plots from northern Peru and Ecuador. Firstly, we examine the floristic patterns using both genus- or species-level data and find that the species-level analysis more clearly distinguishes different plot clusters. Secondly, we compare the patterns and causes of floristic differences at regional and continental scales. At a continental scale, ordination analysis shows that species of Lecythidaceae and Sapotaceae are gradually replaced by species of Arecaceae and Myristicaceae from eastern to western Amazonia. These floristic gradients are correlated with gradients in soil fertility and to dry season length, similar to previous studies. At a regional scale, similar patterns are found within north-western Amazonia, where differences in soil fertility distinguish plots where species of Lecythidaceae, characteristic of poor soils, are gradually replaced by species of Myristicaceae on richer soils. The main coordinate of this regional-scale ordination correlates mainly with concentrations of available calcium and magnesium. Thirdly, we ask at a regional scale within north-western Amazonia, whether soil fertility or other distance dependent processes are more important for determining variation in floristic composition. A Mantel test indicates that both soils and geographical distance have a similar and significant role in determining floristic similarity across this region. Overall, these results suggest that regional-scale variation in floristic composition can rival continental scale differences within Amazonian terra firme forests, and that variation in floristic composition at both scales is dependent on a range of processes that include both habitat specialisation related to edaphic conditions and other distance-dependent processes. To fully account for regional scale variation in continental studies of floristic composition, future floristic studies should focus on forest types poorly represented at regional scales in current datasets such as terra firme forests with high soil fertility from north-western Amazonia.

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41

Honorio Coronado, E. N., T. R. Baker, O. L. Phillips, N. C. A. Pitman, R. T. Pennington, R. Vásquez Martínez, A. Monteagudo, et al. "Multi-scale comparisons of tree composition in Amazonian terra firme forests." Biogeosciences 6, no. 11 (November 30, 2009): 2719–31. http://dx.doi.org/10.5194/bg-6-2719-2009.

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Abstract. We explored the floristic composition of terra firme forests across Amazonia using 55 plots. Firstly, we examined the floristic patterns using both genus- and species-level data and found that the species-level analysis more clearly distinguishes among forests. Next, we compared the variation in plot floristic composition at regional- and continental-scales, and found that average among-pair floristic similarity and its decay with distance behave similarly at regional- and continental-scales. Nevertheless, geographical distance had different effects on floristic similarity within regions at distances <100 km, where north-western and south-western Amazonian regions showed greater floristic variation than plots of central and eastern Amazonia. Finally, we quantified the role of environmental factors and geographical distance for determining variation in floristic composition. A partial Mantel test indicated that while geographical distance appeared to be more important at continental scales, soil fertility was crucial at regional scales within western Amazonia, where areas with similar soil conditions were more likely to share a high number of species. Overall, these results suggest that regional-scale variation in floristic composition can rival continental-scale differences within Amazonian terra firme forests, and that variation in floristic composition at both scales is influenced by geographical distance and environmental factors, such as climate and soil fertility. To fully account for regional-scale variation in continental studies of floristic composition, future floristic studies should focus on forest types poorly represented at regional scales in current datasets, such as terra firme forests with high soil fertility in north-western Amazonia.

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42

Middleton, Richard S., Gordon N. Keating, Philip H. Stauffer, Amy B. Jordan, Hari S. Viswanathan, Qinjun J. Kang, J. William Carey, et al. "The cross-scale science of CO2 capture and storage: from pore scale to regional scale." Energy & Environmental Science 5, no. 6 (2012): 7328. http://dx.doi.org/10.1039/c2ee03227a.

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43

Gleeson, Tom, and Dawn Paszkowski. "Perceptions of scale in hydrology: what do you mean by regional scale?" Hydrological Sciences Journal 59, no. 1 (January 2, 2014): 99–107. http://dx.doi.org/10.1080/02626667.2013.797581.

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44

Förster, A., R. Giese, C. Juhlin, B. Norden, and N. Springer. "The Geology of the CO2SINK Site: From Regional Scale to Laboratory Scale." Energy Procedia 1, no. 1 (February 2009): 2911–18. http://dx.doi.org/10.1016/j.egypro.2009.02.066.

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45

Mammola, Stefano, Shlomi Aharon, Merav Seifan, Yael Lubin, and Efrat Gavish-Regev. "Exploring the Interplay Between Local and Regional Drivers of Distribution of a Subterranean Organism." Diversity 11, no. 8 (July 25, 2019): 119. http://dx.doi.org/10.3390/d11080119.

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Caves are excellent model systems to study the effects of abiotic factors on species distributions due to their selective conditions. Different ecological factors have been shown to affect species distribution depending on the scale of analysis, whether regional or local. The interplay between local and regional factors in explaining the spatial distribution of cave-dwelling organisms is poorly understood. Using the troglophilic subterranean spider Artema nephilit (Araneae: Pholcidae) as a model organism, we investigated whether similar environmental predictors drive the species distribution at these two spatial scales. At the local scale, we monitored the abundance of the spiders and measured relevant environmental features in 33 caves along the Jordan Rift Valley. We then extended the analysis to a regional scale, investigating the drivers of the distribution using species distribution models. We found that similar ecological factors determined the distribution at both local and regional scales for A. nephilit. At a local scale, the species was found to preferentially occupy the outermost, illuminated, and warmer sectors of caves. Similarly, mean annual temperature, annual temperature range, and solar radiation were the most important drivers of its regional distribution. By investigating these two spatial scales simultaneously, we showed that it was possible to achieve an in-depth understanding of the environmental conditions that governs subterranean species distribution.

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46

Nguyen, V. T. V., T. D. Nguyen, and H. Wang. "Regional estimation of short duration rainfall extremes." Water Science and Technology 37, no. 11 (June 1, 1998): 15–19. http://dx.doi.org/10.2166/wst.1998.0425.

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The present study proposes a method for estimating the distribution of short-duration (e.g., 1 hour) extreme rainfalls at sites where data for the time interval of interest do not exist, but rainfall data for longer-duration (e.g., 1 day) are available (partially-gaged sites). The proposed method is based on the recently developed “scale-invariance” (or “scaling”) theory. In this study, the scaling concept implies that statistical properties of the extreme rainfall processes for different temporal scales are related to each other by a scale-changing operator involving only the scale ratio. Further, it is assumed that these hydrologic series possess a simple scaling behaviour. The suggested methodology has been applied to extreme rainfall data from a network of 14 recording raingages in Quebec (Canada). The Generalised Extreme Value (GEV) distribution was used to estimate the rainfall quantiles. Results of the numerical application have indicated that for partially-gaged sites the proposed scaling method is able to provide extreme rainfall estimates which are comparable with those based on available at-site rainfall data.

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47

Zheng, Xin, Sha Zhang, Jiahua Zhang, Shanshan Yang, Jiaojiao Huang, Xianye Meng, and Yun Bai. "Prediction of Large-Scale Regional Evapotranspiration Based on Multi-Scale Feature Extraction and Multi-Headed Self-Attention." Remote Sensing 16, no. 7 (March 31, 2024): 1235. http://dx.doi.org/10.3390/rs16071235.

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Accurately predicting actual evapotranspiration (ETa) at the regional scale is crucial for efficient water resource allocation and management. While previous studies mainly focused on predicting site-scale ETa, in-depth studies on regional-scale ETa are relatively scarce. This study aims to address this issue by proposing a MulSA-ConvLSTM model, which combines the multi-headed self-attention module with the Pyramidally Attended Feature Extraction (PAFE) method. By extracting feature information and spatial dependencies in various dimensions and scales, the model utilizes remote sensing data from ERA5-Land and TerraClimate to attain regional-scale ETa prediction in Shandong, China. The MulSA-ConvLSTM model enhances the efficiency of capturing the trend of ETa successfully, and the prediction results are more accurate than those of the other contrast models. The Pearson’s correlation coefficient between observed and predicted values reaches 0.908. The study has demonstrated that MulSA-ConvLSTM yields superior performance in forecasting various ETa scenarios and is more responsive to climatic changes than other contrast models. By using a convolutional network feature extraction method, the PAFE method extracts global features via various convolutional kernels. The customized MulSAM module allows the model to concentrate on data from distinct subspaces, focusing on feature changes in multiple directions. The block-based training method is employed for the large-scale regional ETa prediction, proving to be effective in mitigating the constraints posed by limited hardware resources. This research provides a novel and effective method for accurately predicting regional-scale ETa.

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48

Praveen, P. S., T. Ahmed, A. Kar, I. H. Rehman, and V. Ramanathan. "Link between local scale BC emissions and large scale atmospheric solar absorption." Atmospheric Chemistry and Physics Discussions 11, no. 7 (July 28, 2011): 21319–61. http://dx.doi.org/10.5194/acpd-11-21319-2011.

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Abstract. Project Surya has documented indoor and outdoor concentrations of black carbon (BC) from traditional biomass burning cook stoves in a rural village located in the Indo-Gangetic Plains (IGP) region of N. India from November 2009- September 2010. In this paper, we systematically document the link between local scale aerosol properties and column averaged regional aerosol optical properties and atmospheric radiative forcing. We report observations from the first phase of Project Surya to estimate the source dependent (biomass and fossil fuels) aerosol optical properties from local to regional scale. Data were collected using surface based observations of BC, organic carbon (OC), aerosol light absorption, scattering coefficient at the Surya village (SVI_1) located in IGP region, and satellite and AERONET observations at the regional scale (IGP). The daily mean BC concentrations at SVI_1 showed the large increase of BC during the dry season (December to February) with values reaching 35 μg m−3. Space based LIDAR data reveal how the biomass smoke is trapped within the first kilometre during the dry season and its extension to above 5 km during the pre-monsoon season. As a result during the dry season, the variance in the daily mean SSA and column aerosol optical properties at the local IGP site correlated (with slopes in the range of 0.85 to 1.06 and R2>0.4) well with the "IGP_AERONET" (mean of six AERONET sites), thus suggesting in-situ observations at few locations can be used to infer spatial mean forcing. The atmospheric forcing due to BC and OC exceeded 20 W m−2 during all months from November to May, leading to the deduction that elimination of cook stove smoke emissions through clean cooking technologies will likely have a major positive impact on health and the regional climate.

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White, Joseph D., and Steven W. Running. "Testing scale dependent assumptions in regional ecosystem simulations." Journal of Vegetation Science 5, no. 5 (October 1994): 687–702. http://dx.doi.org/10.2307/3235883.

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Bastien, Lucas A. J., Nancy J. Brown, and Robert A. Harley. "Contributions to local- and regional-scale formaldehyde concentrations." Atmospheric Chemistry and Physics 19, no. 13 (July 2, 2019): 8363–81. http://dx.doi.org/10.5194/acp-19-8363-2019.

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Abstract. Reducing ambient formaldehyde concentrations is a complex task because formaldehyde is both a primary and a secondary air pollutant, with significant anthropogenic and biogenic sources of volatile organic compound (VOC) precursor emissions. This work uses adjoint sensitivity analysis in a chemical transport model to identify emission sources and chemical reactions that influence formaldehyde mixing ratios in the San Francisco Bay Area and within three urbanized sub-areas. For each of these receptors, the use of the adjoint technique allows for efficient calculation of the sensitivity of formaldehyde to emissions of NOx, formaldehyde, and VOC precursors occurring at any location and time. Formaldehyde mixing ratios are found to be generally higher in summer than in winter. The opposite seasonal trend is observed for the sensitivities of these mixing ratios to formaldehyde emissions. In other words, even though formaldehyde is higher in summer, reducing formaldehyde emissions has a greater impact in winter. In winter, 85 %–90 % of the sensitivity to emissions is attributed to direct formaldehyde emissions. In summer, this contribution is smaller and more variable, ranging from 27 % to 75 % among the receptor areas investigated in this study. Higher relative contributions of secondary formation versus direct emissions are associated with receptors located farther away from heavily urbanized and emission-rich areas. In particular, the relative contribution of biogenic VOC emissions (15 %–43 % in summer) is largest for these receptors. Ethene and other alkenes are the most influential anthropogenic precursors to secondary formaldehyde. Isoprene is the most influential biogenic precursor. Sensitivities of formaldehyde to NOx emissions are generally negative but relatively small in magnitude compared to sensitivities to VOC emissions. The magnitude of anthropogenic emissions of organic compounds other than formaldehyde is found to correlate reasonably well with their influence on population-weighted formaldehyde mixing ratios at the air-basin scale. This correlation does not hold for ambient formaldehyde in smaller urbanized sub-areas. The magnitude of biogenic emissions does not correlate with their influence in either case.

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