Enhancing Air Quality Forecasting in the Metropolitan Zone of the Valley of Puebla: A Comparative Analysis of CAMS and REMA Data

Enhancing Air Quality Forecasting in the Metropolitan Zone of the Valley of Puebla: A Comparative Analysis of CAMS and REMA Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Enhancing Air Quality Forecasting in the Metropolitan Zone of the Valley of Puebla: A Comparative Analysis of CAMS and REMA Data Javier Omar Castillo-Miranda, José Carlos Mendoza-Hernández, José Agustín García-Reynoso, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3775064/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Air quality in the Metropolitan Zone of the Valley of Puebla has shown in previous years that suspended particles less than 10 micrometers (PM 10 ) and less than 2.5 micrometers (PM 2.5 ) present a health risk. The automatic air quality monitoring system in Puebla measures PM 10 and PM 2.5 at five stations in the Puebla and Coronango municipalities. These measurements allow for the determination of the Air and Health Index according to the NOM-172-SEMARNAT-2019 standard for these pollutants. The advancement of satellite monitoring techniques represents an opportunity for air quality management where terrestrial measurements are scarce. However, to obtain reliable data, it is necessary to validate the satellite data with ground measurements from georeferenced monitoring stations. The operational implementation forecast of the Copernicus Atmospheric Monitoring Service (CAMS) allows for conducting out atmospheric pollution exploration processes. An analysis of this forecast data determined that the Persistence forecast is better than the CAMS forecast overall for both PM 10 and PM 2.5 . However, the CAMS forecast can be employment for a preliminary evaluation in the prediction of PM 2.5 due to the success in the comparison criteria of the dichotomous statistics ACCURACY, POD, FAR, POFD, SR, TS, ETS, HSS and ORSS. PM2.5 and PM10 air quality Copernicus CAMS Contingency tables Dichotomous statistics and satellite monitoring Figures Figure 1 Figure 2 1. Introduction A study on Air Quality in Latin America cities underscores the urgent need of improve the environment implementing public policies. According to the analysis, Brazil had the highest number of premature deaths due to air pollution, with 24 thousand cases in 2008. Mexico followed closely in second place with 15 thousand deaths in the same year. The CAI study also includes the cities of Mexico City, Puebla, Monterrey, Guadalajara, Ciudad Juárez, and León (CAI, 2013 ). In 2011, a startling statistic emerged 64% of asthma-related deaths affected children under the age of five years across the country. Furthermore, specific states recorded the highest number of asthma-related deaths in children, with Veracruz with 21 cases, Chiapas and Puebla reporting 16 cases. Adding to these concerns, the Organization for Economic Cooperation and Development (OECD) predicts that if the same trend in air pollution continues, it will turn into the main reason of early death in the world (REDIM, 2013 ). The study area encompasses the Metropolitan Zone of the Valley of Puebla (MZVP). The municipality of Puebla is situated within this region, located in the central-western part of the State of Puebla. It is positioned at a north latitude of 19°02'38" and a west longitude of 98°11'50"; with an elevation of 2,137 meters above sea level. Puebla shares its borders with various regions: to the north, it adjoins the state of Tlaxcala; to the east, it connects with the municipalities of Amozoc, Cuautinchán, Tzicatlacoyan and Tepatlaxco de Hidalgo; to the south, it abuts Teopantlán and Huehuetlán el Grande, while to the west, it shares boundaries with Cuautlancingo, San Pedro Cholula, Ocoyucan and San Andrés Cholula. The City Council covers an area of 524.31 km 2 , with a population of 1’576,259 people (INEGI, 2017 ). The air quality in the Metropolitan Zone of the Valley of Puebla faces significant challenges, primarily stemming from the rising number of vehicles. This issue is exacerbated by the absence of sustainable mobility policies aimed at reducing environmental impacts and the limited regulations governing pollutant emissions from local industries. The Municipality of Puebla is grappling with severe mobility problems due to unregulated urban expansion and the influence of suburban municipalities. This has led to increased commute times and expenses for residents traveling between their homes, workplaces, and essential services. Consequently, there has been a notable surge in the use of private cars and public transportation. While the Urban Articulated Transport Network (RUTA) represents a viable alternative, the traditional model of individual car ownership still persists. This model suffers from various shortcomings, including long travel distances, delays, accessibility issues, and safety concerns, all of which contribute to road congestion. This congestion can be attributed to the staggering growth of the vehicle fleet, which has increased by up to 89.2% as of 2020 compared to the year 2000. This translates to an additional 251,673 vehicles, with an average annual growth rate of 3.24% (PMD, 2021 ). Another concerning factor impacting air quality in the study area is the degradation of forest ecosystems. This includes illegal logging activities aimed at harvesting carbon and wood theft within the Malinche National Park, in the General Lázaro Cárdenas del Río State Park, Flor del Bosque. Additionally, extensive livestock farming within the Sierra del Tentzo State Reserve and urban expansion around the Valsequillo Wetland State Park (PMD, 2021 ) contribute to ecosystem disruption. Furthermore, between 1984 and 2018, the municipality witnessed varying degrees of environmental deterioration. Approximately 25.2% of its forested area, equivalent to around 2,800 hectares, suffered damage. Conservation forests also declined from 27–15.8%, covering roughly 1,689 hectares (INEGI, 2018 ). Climate is a factor that also influences air quality. The municipality of Puebla experiences a temperate subhumid climate characterized by a summer rain regime. The speed and direction of the wind also play a crucial role in determining pollutant dispersion. The prevailing winds in the region are oriented from the NNE to SSW with an average velocity of 1.6 m/s. The highest wind speed is typically recorded in January at 2.4 m/s, while the lowest occurs in December, dropping to 0.5 m/s. The movement of these winds interacts with the cold mass of the La Malinche volcano, resulting in the formation of cold air currents that affect the entire surface of the Puebla Municipality. The rainy season spans from May to October, with peaks in June and September. Precipitation is distributed almost uniformly across the entire territory. The minimum temperature is recorded at the upper part of the La Malinche volcano, where it can drop to as low as 5°C. Conversely, in the southern zone of the municipality, the average annual temperature reaches a more temperate 18°C (PMD, 2011 ). The assessment of air pollution in major cities has traditionally relied on ground-based measurements. However, the advancements in satellite remote sensing present a significant opportunity. This technology allows for the establishment of large-scale patterns showcasing the impact of both natural and human-generated emissions on the overall composition of the Earth's atmosphere at a global scale. Furthermore, satellite monitoring significantly augments the influx of atmospheric data, emphasizing the necessity for global systems to harness this data for comprehensive analysis (Hollingsworth et al., 2008 ). 1.1. Atmospheric composition prediction system for the Copernicus Atmospheric Service The European Copernicus program was preceded by the Global Environment and Security Monitoring Project (GEMS). Furthermore, the integration of other Monitoring of Atmospheric Composition and Climate (MACC and MACC-II) projects (Hollingsworth et al., 2008 ) made possible the development of a new and innovative prediction system for atmospheric structure, destined for the Copernicus Atmospheric Service and supported by the European Commission. The system carries out global air quality and pollution forecasts. Their predictions constitute warning systems to preserve human health. Also, it predicts exposure to ultraviolet rays, attainable solar energy and dangers occasioned by dust storms. The Copernicus Atmosphere Monitoring Service (CAMS) uses satellites to monitor and forecast aerosols and trace atmospheric gases at a global level. It is uses the combination of satellite vigilance of atmospheric conformation with, last generation of atmospheric models with applications in air pollution (Hollingsworth et al., 2008 ; Flemming et al., 2013 ). Data assimilation is a method that combines model findings, joining satellite observations and in-situ observations. This technique is employed in numerical weather prediction to acquire a trustworthy and quality basis for the model forecast. For the Copernicus Atmospheric Service, the Integrated Forecast System (IFS) of the European Center for Medium-Range Weather Forecasts (ECMWF). It has been suitable to integrate satellite observations concerning atmospheric composition (Flemming et al., 2013 ) and incorporate procedures for aerosol modeling (Morcrette et al., 2009 ; Benedetti et al., 2009 ). The gathering of distinct inventories of anthropogenic and biogenic emanations on a global and regional scale (Granier et al., 2011 ) constitutes the basis of the estimates of modern regional models such as CHIMERE, EMEP, EURAD, LOTOS, MATCH, MOCAGE, and SILAM. These operate in the European region for the Copernicus Atmospheric Service (Kukkonen et al., 2012 ). Therefore, the regional results favor enough users to generate air quality predictions at the urban and street level. An important application is related to the formation of secondary aerosols, which is an important contribution to fine particles (PM2.5) (Flemming et al., 2013 ). In the case of North America, the results of the global model that uses a 0.4x0.4 degree mesh are used. 1.2. Influence of air pollution on health Ambient air pollution in 2015 contributed to the global magnitude of morbidity. It is due to the increase in the age of the population in the last 25 years. Weighted average PM 2.5 concentrations with an approximate resolution of 11 km × 11 km. These were supported by satellites, chemical transport models and ground-level measurements. Likewise, in 2015, PM 2.5 turned out to be the fifth risk factor for mortality, causing 4.2 million deaths and characterizing 7.6% of global deceases (Cohen et al., 2017 ). Ambient PM 2.5 is related to 412,000 anticipated deceases due to risk exposure in more than 41 European countries (EEA, 2019). The American Cancer Society reports that air pollution in metropolitan areas is a risk factor for heart disease and causes death. Likewise, mortality from long-term exposure to suspended particles (PM) is linked to ischemic heart disease, arrhythmias, heart failure, and cardiac arrest. An increase of 10 µg/m 3 in PM concentration causes an 8–18% increase in mortality risk (Pope et al., 2004 ). The increase in mortality and morbidity rates in developed and developing countries is associated with air pollution. It has caused air quality standards to become more severe. An evaluation of the effect of PM 10 in 20 of the biggest urban areas in the United States in the period from 1987 to 1994, demonstrate that the concentration level of PM 10 is related to the death rate, for various causes and for cardiovascular and respiratory diseases. That led to a 0.51% increase in the overall mortality rate for every 10 µg/m 3 increase in PM 10 concentrations. Meanwhile, the predicted growth in the relative ratio of death associated with cardiovascular and respiratory was 0.68% percent when the PM 10 concentration level increased by 10 µg/m 3 . These findings inform measures to monitor levels of breathable particles in outdoor air (Samet et al., 2000 ). Satellite data support the generation of a model to examine the relative effects on mortality from exposure to PM 2.5 in short periods. The model couples the optical depth of aerosols and measurements at ground level in Massachusetts, United States, from 2000 to 2008. Likewise, the increase in the concentration of PM 2.5 by 10 µg/m 3 , increases mortality by 2.8%. On the other hand, long-term contact showed an increase in the odds ratio (OR) of 1.6, when the level of PM 2.5 increased by 10 µg/m 3 . Local PM 2.5 presented an OR of 1.4. The methodology used makes it possible to analyze larger areas, even far from urban and rural areas, with better spatial quality (Kloog et al., 2013 ). An assessment in 2016 established that 95% of the globe inhabitants existed in zones where PM 2.5 concentration degree surpass the World Health Organization's (WHO) guideline of an annual average of 10 µg/m 3 (WHO, 2006). This assessment is the product of the agreement between the results of the data integration model for air quality (DIMAQ) and the global population estimate cell by cell (Shaddick, 2018). DIMAQ was carried out in the Bayesian hierarchical modeling (BHM) environment (Shaddick, 2017). 1.3 Air pollution in the Metropolitan Zone of the Puebla Valley The Red Estatal de Monitoreo Atmosférico (State Atmospheric Monitoring Network, REMA) is a mechanism that allows reporting on air quality in a given area. It began operating during 2000, with the purpose of measuring atmospheric pollutants and meteorological parameters. As well as determining the influence that these have on the Municipality of Puebla and its metropolitan area. The metropolitan region consists of municipalities: San Pedro Cholula, San Andrés Cholula, Cuautlancingo, Coronango and Amozoc (REMA, 2023 ). The State Atmospheric Monitoring Network; is managed by the Secretariat of Environmental Sustainability and Territorial Planning of Puebla State Government. It is comprises of five continuous monitoring stations: Technological University of Puebla, Agua Santa, Ninfas, Benemérito Instituto Normal del Estado located in the Municipality of Puebla and the Velodrome station in the municipality of Coronango. These stations determine the criteria pollutants: carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone and particles with an equivalent aerodynamic diameter equal to or less than 10 micrometers (PM 10 ) and 2.5 micrometers (PM 2.5 ). Additionally, it integrates the measurement of wind way and speed, temperature and relative humidity (INECC, 2019 ). Air quality in the Metropolitan Zone of the Valley of Puebla is evaluated by the percentage of days of the year that the regulated limit is exceeded [NOM-025-SSA1- 2014 , ( 2014 )]. PM₁₀ particles exceeded the 24-hour limit on 24% of the days and PM₂.₅ particles exceeded 2.5%, both at the 24-hour limit and the annual average; In Coronango, PM₁₀ particles exceeded the 24-hour limit by 0.4%. During 2018, at least one Environmental Health regulation [NOM-172-SEMARNAT- 2019 , ( 2019 )] was violated on 32% of the days in Puebla and on 0.3% of the days in Coronango (INECC, 2019 ). The air quality standard in the Municipality of Puebla for PM 10 established in 24 hours [NOM-025-SSA1- 2014 , ( 2014 )], exceeded 72 days in 2019. In the municipality of Coronango with the information available, the limit was exceeded on 19 of the 259 days. In both municipalities, the highest concentrations of PM 10 occurred between January and May. Compared to 2018, a slight decrease was recorded in the City of Puebla and an increase in Coronango in accordance with the 24-hour limit (INECC, 2020 ). In relation to PM 2.5 in Puebla in 2019, there were 250 days of good air quality, 101 with acceptable standard, 10 with poor quality and 4 with very poor quality, according to [NOM-172-SEMARNAT- 2019 , ( 2019 )]. The highest concentrations of PM 2.5 were also observed between January and May (INECC, 2020 ). During the hot and dry period from February to May 2021, PM 10 reached levels of concentration on 59 days that exceeded the maximum permitted limit. It is in accordance with the provisions of the regulations on particles [NOM-025-SSA1- 2014 , ( 2014 )]. On the other hand, the warm-humid season from June to October decreased the magnitude of PM 10 concentrations. Meanwhile, PM 2.5 showed that the maximum permissible limit was exceeded in three days [NOM-025-SSA1- 2014 , ( 2014 )]. Air quality was mainly recorded as fair (SMADSOT, 2021). PM 10 is formed naturally through volcanic emissions, forest fires, soil dust and bioaerosols. They can also originate from human activities such as vehicle combustion and the construction industry (SEDEMA, 2020 ). PM 10 affects health directly, causing lung diseases such as asthma and chronic obstructive pulmonary disease (COPD) (MacNee and Donalson, 2000 ). Decreased visibility, in addition to the impact of nutrients in soils, bodies of water, and forests due to deposition processes, are the environmental consequences that PM 10 can cause in the environment (U.S. EPA, 2020). PM 2.5 contains a smaller aerodynamic diameter than PM 10 and is part of the latter, contributing around 52% to its total mass. Environmental PM 2.5 affects the health of the population, harming the cardiovascular system. It includes heart attacks and strokes. It also damages the respiratory system by causing asthma attacks and cancer. It can have consequences such as hospitalizations, visits to the emergency room and possibly premature death (SEDEMA, 2020 ). 2. Materials and methods Figure 1 illustrates the methodology employed to acquire data, generate contingency tables, and derive results. 2.1 Air quality data The process commences within the Atmosphere Data Store (ADS), serving as the primary gateway for data sourced from the Copernicus Atmospheric Monitoring Service (CAMS). Within this system, access is granted to the CAMS global atmospheric composition forecasts section. For this analysis, the Particulate Matter variables, namely PM 2.5 and PM 10 , are considered at a single level. The temporal scope spans from December 31, 2020, to December 31, 2021, with daily downloads scheduled at 00:00 UTC, spanning a delivery window from 0 to 23 hours. The Download Type selected is CAMS forecast, and the geographic area of interest corresponds to a restricted zone defined by specific geographic coordinates encompassing the Metropolitan Zone of the Valley of Puebla. The data retrieved is formatted in netCDF (CAMS, 2023 ). To process this data, a Python script is employed. This script extracts PM 10 and PM 2.5 values in kg/m³ and converts them to µg/m³ using the nearest neighbor method based on the coordinates of the monitoring stations. 2.1.1 CAMS Forecast and the Air and Health Index Database The Air and Health Indices (AHI) within the CAMS Copernicus forecast are calculated following the procedure described in the standard [NOM-172-SEMARNAT- 2019 , ( 2019 )]. A Python script is employed for this purpose, resulting in the generation of 8,760 AHI records per station. The CAMS forecast Air and Health Index database comprises a total of 43,800 records, corresponding to the estimated values for the five stations of the Monitoring Network (REMA). The concentrations of PM 2.5 and PM 10 provided by the CAMS forecast are define within a grid with approximate dimensions of 40x40 km, whereas the measurements from the stations are point-specific. This discrepancy introduces a systematic error, as CAMS concentrations tend to underestimate observations from surface monitoring stations. To rectify this error, scaling factors are calculated through difference and ratio method to obtain an adjusted CAMS value for the Air and Health Index. The procedure includes the extraction of maximum daily AHI values from both CAMS and REMA for each month of the study year. The difference scaling factor entails subtracting the REMA AHI from the CAMS AHI on a daily basis and obtaining a monthly average value. This data is then added to the daily CAMS AHI to determine the difference-adjusted CAMS value. The ratio scaling factor, on the other hand, involves dividing the REMA AHI by the CAMS AHI for each day and obtaining a monthly average value. This value is then multiplied by the daily CAMS AHI to arrive at the ratio-adjusted CAMS value. Subsequently, a comparison of these scaling factors is carried out considering both difference and ratio methods. This comparison yields difference- and ratio-adjusted CAMS values, which are used to predict the occurrence of events characterized by poor air quality, based on the Air and Health Index (AHI) observed. Specifically, the AHI is deemed to be greater than 45 (µg/m 3 ) for PM 2.5 and greater than 75 (µg/m 3 ) for PM 10 , as per the 12-hour weighted moving average interval [NOM-172-SEMARNAT- 2019 , ( 2019 )]. This comprehensive procedure allows us to determine which scaling factor offers the best fit for predicting poor air quality events at the stations of the Atmospheric Monitoring Network. 2.1.2 Air and Health Indices Database of measurements at ground level The database comprises average hourly measurements of PM 10 and PM 2.5 , obtained from monitoring stations within the Automatic Air Quality Monitoring Network (SMADSOT, 2023 ). Please refer to Table 1 for sample names, acronyms, and geographic coordinates, and for a visual representation of their geographic locations, consult Fig. 2 . Table 1 Monitoring stations of the Metropolitan Zone of the Valley of Puebla No. Station Acronyms Coordinates (Latitude, Longitude) 1 Agua Santa STA 18.9874, -98.2497 2 Benemérito Instituto Normal del Estado BINE 19.0673, -98.2245 3 Parque de las Ninfas NINFAS 19.0413, -98.2142 4 Universidad Tecnológica de Puebla UTP 19.0567, -98.1517 5 Velódromo VELODROMO 19.1158, -98.2776 The average hourly measurements of PM 10 and PM 2.5 establish the Air and Health Index (AHI) database for PM 10 and PM 2.5 corresponding to the year 2021. It can be consulted on the official page of the State Network of Atmospheric Monitoring (REMA) (REMA, 2023 ). The AHI database for PM 2.5 integrates: 4,045 records for Agua Santa Station (STA), 4,567 records for BINE Station, 7,200 records for NINFAS station, 6,822 records for UTP station and 0 records for VELODROME station. The AHI database for PM10 contains: 4,680 records for Agua Santa Station (STA), 7,622 records for BINE Station, 7,758 records for NINFAS station, 6,982 records for UTP station and 1,135 records for VELODROME station. 2.2 Procedures for dichotomous forecasts The Persistence forecast serves as a baseline for comparison with the CAMS forecast, enabling us to assess its performance. We utilize 2x2 contingency tables to categorize days of the year into those that exceed or do not exceed the Air and Health Index across three data groups: Measurements, the Persistence forecast, and the CAMS forecast. Dichotomous statistics ensure that events are classified into two categories: "yes, the event occurs" or "no, the event does not occur." To evaluate this type of forecast, we employ 2x2 contingency tables. These tables comprise four cells, representing the following categories for a Persistence forecast: Successes (forecasted yes, observed yes), failures (forecasted no, observed no), false alarms (forecasted yes, observed no), and correct negatives (forecasted no, observed no). The total counts of observed and predicted events and non-events are located along the bottom and right-hand corner of the contingency table, which is presented in Table 2 and referred to as "marginal distribution (WWRP/WGNE, 2017 ; Gold et al., 2020 ). Table 2 Contingency table Observed Yes No Total Forecast Yes Hits False alarms Forecast yes No Misses Correct negatives Forecast no Total Observed yes observed no Total The contingency table serves as a valuable tool for pinpointing the types of errors in forecasting. An ideal forecasting method should yield only hits and correct negatives, with no misses or false alarms. By combining elements from the contingency table, we can derive dichotomous statistics that provide insights into various aspects of forecast performance. Table 2 displays the optimal values of these dichotomous statistics, which are described below. Precision (fraction correct) Indicates what portion of the forecasts were correct. Accuracy = \(\frac{hits + correct negatives}{total}\) (1) Bias score (frequency bias) Compares the frequency of the predicted events and the frequency of the observed events. Determines whether there is a tendency to under forecast (BIAS 1) events. BIAS = \(\frac{hits + false alarms}{hits + misses}\) (2) Detection probability (hit rate) Indicates the fraction of observed "yes" events that were correctly predicted. POD = \(\frac{hits}{hits + misses}\) (3) False alarm rate Expresses the fraction of the forecast events that will occur, but did not occur. FAR = \(\frac{false alarms}{hits+false alarms}\) (4) Probability of false detection (false alarm ratio) Considers the part of events not observed; were incorrectly predicted as “yes.” POFD = \(\frac{false alarms}{correct negatives + false alarms}\) (5) Success Ratio Considers the fraction of events predicted “yes” that were correctly observed. SR = \(\frac{hits}{hits+false alarms}\) (6) Threat score (critical success rate) Determines the part of observed and/or predicted events that were suitably predicted. TS = \(\frac{hits}{hits+misses+false alarms}\) (7) Equitable threat score (Gilbert skill score) Quantifies the portion of observed and/or predicted episodes that were suitably predicted, coupled by hits related to aleatory probability. ETS = \(\frac{{hits-hits}_{random}}{{hits + misses+false alarms-hits }_{random}}\) (8) where: $${hits}_{random }= \frac{(hits+misses)(hits+false alarms)}{total}$$ Hanssen and Kuipers differentiate (true ability statistic, Peirce skill score) Indicates the success of the forecast separating “yes” events from “no” events. HK = \(\frac{hits}{hits+misses}\) - \(\frac{false alarms}{false alarms + correct negatives}\) (9) Heidke skill score (Cohen's K) Establishes the share of correct predictions afterward removing those forecast that would be correct due to chance alone. HSS = \(\frac{{\left(hits+correct negatives\right)-\left(expected correct\right)}_{random}}{{N-\left(expected correct\right)}_{random}}\) (10) Where: \({\left(expected correct\right)}_{random} = \frac{1}{N}[\left(hits+misses\right)\left(hits+false alarms\right)+(correct negatives+misses\left)\right(correc negatives+false alarms)\) ] Odds Ratio Skill Score (Yule’s Q) Indicates how far the forecast was ahead of chance. ORSS = \(\frac{hits * correct negatives - misses *false alarms}{hits * correct negatives + misses *false alarms}\) (11) Table 3 shows the optimal values of the dichotomous statistics. Table 3 Metric, range and associated optimal values Metric Range Optimal value Accuracy 0 to 1 1 BIAS 0 to ∞ 1 POD 0 to 1 1 FAR 0 to 1 0 POFD 0 to 1 0 SR 0 to 1 1 TS 0 to 1 1 ETS -1/3 to 1 1 HK -1 to 1 1 HSS -1 to 1 1 ORSS -1 to 1 1 2.2.1 Evaluation of the CAMS forecast using Air and Health Indices based on ground measurements It were perform a dichotomous evaluation using the databases of the Air and Health Indices (AHI) for PM 2.5 and PM 10 , incorporating data from the Monitoring Network measurements and the CAMS forecast model. The dichotomous verification is based on the following premise: it assesses whether the Integrated Air Quality Index (AHI) exceeds 45 for PM 2.5 and 75 for PM 10 . This evaluation results in a 'yes' or 'no,' 'true' or 'false' determination for both the observed and modeled values. The AHI indicates that air quality is considered poor and poses a high risk when the 12-hour weighted moving average surpasses 45 (µg/m 3 ) for PM 2.5 and 75 (µg/m 3 ) for PM 10 , in accordance with [NOM-172-SEMARNAT- 2019 , ( 2019 )]. Next, a 2x2 contingency table is constructed to identify the days of the year when observations either exceed or do not exceed the AHI, as well as the days when CAMS forecasts exceed or do not exceed the AHI. These four values are then used to derive the dichotomous statistics, which are described previously. 3. Results and discussion 3.1 Comparison of the REMA persistence forecast with the CAMS forecast The comparison between the REMA AHI persistence forecast and the CAMS AHI forecast adjusted by ratio allows us to assess the performance of CAMS relative to the REMA Persistence forecast. This evaluation is presented in Tables 4 and 5 . Table 4 reveals that the adjustment using the CAMS ratio for PM 2.5 results in a higher number of events where the AHI exceeds 45 µg/m³ at the Agua Santa, Ninfas, and UTP stations. However, the Persistence forecast outperforms CAMS only at the BINE station within the Atmospheric Monitoring Network. Table 4 REMA Persistence Forecast and CAMS Forecast Adjusted by PM 2.5 Ratio REMA PM 2.5 Persistence Forecast CAMS PM 2.5 forecast Ratio adjustment No. of times AHI > 45 µg/m 3 No. of times AHI > 45 µg/m 3 Month Agua Santa BINE Ninfas UTP Velodromo Agua Santa BINE Ninfas UTP Velodromo January 0 0 2 2 0 0 3 5 2 0 February 1 3 0 1 0 0 0 11 1 0 March 10 4 3 2 0 0 1 9 3 0 April 15 4 1 2 0 18 14 8 11 0 May 5 0 0 0 0 11 1 2 10 0 June 0 0 0 1 0 0 1 0 0 0 July 0 2 0 1 0 0 1 0 1 0 August 0 0 0 0 0 0 0 0 0 0 September 0 0 0 1 0 0 0 0 0 0 October 0 1 0 0 0 0 0 0 0 0 November 4 1 0 0 0 8 0 0 0 0 December 17 0 2 1 0 18 0 0 4 0 Table 5 shows that the adjustment by CAMS ratio for PM 10 includes a greater number of times that the AHI is greater than 75 µg/m 3 at the Agua Santa and BINE stations. The Persistence forecast is higher at the UTP and Velodromo stations and with a similar value at the Ninfas station of the Atmospheric Monitoring Network. Table 5 REMA Persistence forecast and CAMS forecast adjusted by PM 10 ratio REMA PM 10 Persistence Forecast CAMS PM 10 forecast Ratio adjustment No. of times AHI > 75 µg/m 3 No. of times AHI > 75 µg/m 3 Month Agua Santa BINE Ninfas UTP Velodromo Agua Santa BINE Ninfas UTP Velodromo January 0 6 19 22 5 0 16 24 26 15 February 11 18 24 23 9 14 21 26 25 4 March 25 19 25 26 12 22 20 24 25 7 April 27 18 28 23 1 26 21 27 25 1 May 10 3 6 16 0 16 11 10 19 0 June 1 1 2 4 0 2 1 1 0 0 July 2 7 9 14 0 0 10 14 12 0 August 0 0 1 4 0 0 3 4 4 0 September 0 2 1 5 0 0 2 0 4 0 October 2 2 1 1 0 0 3 0 0 November 9 5 5 9 0 15 8 4 7 0 December 23 18 21 23 0 28 23 24 25 0 3.2 Comparison of dichotomous statistics of Copernicus CAMS and REMA Persistence Dichotomous statistics describe characteristics of forecast performance. These were obtained from the Air and Health Indices of [PM 10 ] and [PM 2.5 ] of Copernicus CAMS adjusted by ratio and from the Air and Health Indices of [PM 10 ] and [PM 2.5 ] of REMA in the ZMVP. The evaluation of dichotomous statistics is carried out according to the results available in tables A5, A6, A7, A8, A9, A10, A11, A12 and A13 contained in supplementary information . The purpose is to establish the performance of CAMS with respect to the REMA Persistence Forecast. Table 6 represents the percentage proportion of values that register monthly the dichotomous statistics at the monitoring stations with respect to PM 2.5 . The percentage proportion indicates the number of hits close to or equal to the optimal value of the statistic in Table 3 . The comparison considers when CAMS is better than Persistence, CAMS is the same as Persistence, and Persistence is better than CAMS. The results indicate a higher number of statistics with a higher percentage when CAMS equals Persistence: ACCURACY, POD, FAR POFD, SR, TS and ORSS. Coincidence in the percentage value in the ETS and HSS statistics when CAMS is better than persistence, and Persistence is better than CAMS. Persistence is better than CAMS for BIAS and HK statistics. Table 6 Percentage comparison of dichotomous statistics of Copernicus CAMS and REMA Persistence adjusted by ratio for PM 2.5 Performance of Copernicus CAMS ratio adjustment versus REMA persistence forecast for PM 2.5 ACCURACY (%) BIAS (%) POD (%) FAR (%) POFD (%) SR (%) TS (%) ETS (%) HK (%) HSS (%) ORSS (%) CAMS better than Persistence 27.08 4.17 4.17 4.17 18.75 6.25 6.25 18.75 16.67 20.83 4.17 CAMS equal than Persistence 37.50 2.08 27.08 16.67 41.67 16.67 25.00 6.25 6.25 2.08 14.58 Persistence better than CAMS 35.42 35.42 10.42 12.50 35.42 10.42 12.50 18.75 18.75 20.83 12.50 Table 7 reveal the network stations with the best monthly value of dichotomous statistics. It stands out that in March the UTP and BINE stations coincide in the FAR, SR, TS and HSS statistics. While in the month of July, UTP and BINE agree on POD, FAR, SR, TS and ORSS. In the month of August and September, the Agua Santa, BINE, and Ninfas stations coincide in the ACCURACY and POFD statistics. In general, the stations with the highest number of hits are UTP with 45 and BINE with 24. Table 7 Comparison of stations with the best percentage of PM 2.5 CAMS dichotomous statistic value Stations with the best percentage value of dichotomic statistics of PM 2.5 CAMS Month ACCURACY BIAS POD FAR POFD SR TS ETS HK HSS ORSS January A U U A, U U U U February U U A, U U U U March U U, B U, B U, B B U, B U April U, N N N N N U U N May June A, N U A, N U U U July A, N U, B U, B A, U, N U, B U, B B B U, B August A, N, B, U A, N, B, U September A, N, B U A, B, N U U U October A, N, U B U, N B B B, A November N, U B U, N B, A B December B U, N, A U U U, N N N U A = AGUA SANTA B = BINE N = NINFAS U = UTP Table 8 represents the percentage proportion of values that the dichotomous statistics record monthly at the monitoring stations with respect to PM 10 . The comparison considers when CAMS is better than Persistence, CAMS is the same as Persistence, and Persistence is better than CAMS. The results indicate for all statistics a higher percentage when Persistence is better CAMS. Table 8 Percentage comparison of dichotomous statistics of Copernicus CAMS and REMA Persistence adjusted by ratio for PM 10 Performance of Copernicus CAMS ratio adjustment versus REMA persistence forecast for PM 10 ACCURACY (%) BIAS (%) POD (%) FAR (%) POFD (%) SR (%) TS (%) ETS (%) HK (%) HSS (%) ORSS (%) CAMS better than Persistence 25.00 3.33 23.33 16.67 20.00 15.00 18.33 26.67 26.67 26.67 18.33 CAMS equal than Persistence 21.67 3.33 15.00 11.67 31.67 15.00 13.33 1.67 1.67 1.67 10.00 Persistence better than CAMS 53.33 73.33 40.00 40.00 48.33 38.33 48.33 51.67 51.67 51.67 40.00 4. Conclusions In conclusion, our study presents a substantial step forward in air quality forecasting within the Metropolitan Zone of the Valley of Puebla. We have successfully implemented a procedure that effectively addresses systematic errors in data retrieval from the Copernicus CAMS forecast, enabling us to make precise predictions of poor air quality events in alignment with the standards set forth in NOM-172-SEMARNAT-2019. Our findings clearly demonstrate that the adjustment by ratio, applied to both PM 10 and PM 2.5 , significantly enhances the forecasting performance of CAMS. This adjustment surpasses the REMA persistence forecast for some periods and stations in accurately predicting unsatisfactory air quality events attributed to PM 10 and PM 2.5 . Additionally, the monthly percentage comparison of dichotomous statistics reveals nuanced insights. While Persistence outperforms CAMS in most cases for both PM 2.5 and PM 10 , several specific statistics, including ACCURACY, POD, FAR, POFD, SR, TS, ETS, HSS, and ORSS, can be employed for a preliminary evaluation of CAMS PM 2.5 forecasting performance at the stations of the State Atmospheric Monitoring Network. Notably, these statistics demonstrate acceptable percentages. Further analysis of PM 2.5 forecasts from the UTP and BINE stations as diagnostic indicators of poor-quality ambient PM 2.5 events indicates that they likely represent regional issues. These stations exhibit the highest number of correct hits in the dichotomous statistics throughout the year. Conversely, the Agua Santa and Ninfas stations appear to be more representative of local issues, potentially influenced by local emissions, roads, and industrial factors. We acknowledge that the non-operational status of certain REMA stations for extended periods resulted in insufficient data for the study. This led to a shortage of PM 2.5 data by 48.38% and PM 10 data by 37.12%, limiting the comprehensiveness of our analysis. The significance of our study lies in its pioneering approach. It marks the first time that such a comprehensive comparison has been conducted on the American Continent, specifically in Mexico. These findings provide critical insights and suggest avenues for further improvement, including enhancing resolution, incorporating detailed emissions inventories, and accounting for local meteorological variables. Our research lays the foundation for enhanced air quality forecasting in the region, offering valuable information for policy decisions and further studies aimed at improving air quality in the Metropolitan Zone of the Valley of Puebla. Declarations Acknowledgments Javier Omar Castillo-Miranda expresses gratitude to CONSEJO NACIONAL DE HUMANIDADES CIENCIAS Y TECNOLOGÍAS (CONAHCYT) for the postdoctoral fellowship that supported this study. Additionally, both Javier Omar Castillo-Miranda and José Carlos Mendoza-Hernández extend their thanks to the College of Environmental Engineering within the Faculty of Chemical Engineering at Benemérita Universidad Autónoma de Puebla. Thanks are also extended to the Air Quality Management Directorate of the Ministry of the Environment, Sustainable Development and Territorial Planning of the Government of Puebla, for providing valuable information for this project. Author contributions Javier Omar Castillo-Miranda: Formulation of aims, design of methodology, application of statistical to analyze study data, presentation of the data, writing the initial draft. José Carlos Mendoza-Hernández: Activity planning and execution, provision of study materials and computing resources, preparation of the published work. José Agustín García-Reynoso: Programming and designing computer programs, verification of research results. Gabriela Pérez-Osorio: Management and coordination for the research activity planning and execution. Data availability The datasets generated and analyzed in this study are available from the corresponding author upon reasonable request. Ethical approval All authors have read, understood, and have complied as applicable with the statement on “Ethical responsibilities of Authors” as found in the Instructions for Authors. Competing interests The authors declare no competing interests. Funding No funding was obtained for this study. References Benedetti, A., Morcrette, J.-J., Boucher, O., Dethof, A., Engelen, R. J., Fisher, M., Flentje, H., Huneeus, N., Jones, L., Kaiser, J.W., Kinne, S., Mangold, A., Razinger, M., Simmons, A.J., Suttie, M. (2009). Aerosol Analysis and Forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System. Part II: Data Assimilation; J. Geophys. Res, 114, D13205 . https://doi.org:10.1029/2008JD011115 . CAI, (2013). 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I., Monteiro, A., Hirtl, M., Tarvainen, V., Boy, M., Peuch, V.-H., Poupkou, A., Kioutsioukis, I., Finardi, S., Sofiev, M., Sokhi, R., Lethinen, K., Karatzas, K., San José, R., Asthita, M., Kallos, G., Schaap, M., Reimer, E., Jakobs H., Eben, K. (2012). A review of operational, regional-scale, chemical weather forecasting models in Europe; Atmos. Chem. Phys., 12 , 1–87. https://doi.org/10.5194/acp-12-1-2012 . MacNee, W., Donalson, K. (2000). Exacerbations of COPD: environmental mechanisms. Chest. 2000; 117(5 Suppl 2):390S-7S. https//doi: 10.1378/chest.117.5_suppl_2.390s . PMID: 10843983. Morcrette, J.-J., Boucher, O., Jones, L., Salmond, D., Bechtold, P., Beljaars, A., Benedetti, A., Bonet, A., Kaiser, J. W., Razinger, M., Schulz, M., Serrar, S., Simmons, A. J., Sofiev, M., Suttie, M., Tompkins, A.M., Untch, A. (2009). Aerosol Analysis and Forecast in the ECMWF Integrated Forecast System. Part I: Forward Modeling; J. Geophys. Res., 115 , D06206. https://doi.org/10.1029/2008JD011235 . NOM-025-SSA1-2014, (2014). NORMA Oficial Mexicana NOM-025-SSA1-2014, Salud ambiental. Valores límite permisibles para la concentración de partículas suspendidas PM10 y PM2.5 en el aire ambiente y criterios para su evaluación. Retrieved date April 10, 2023, from https://dof.gob.mx/nota_detalle.php?codigo=5357042&fecha=20/08/2014#gsc.tab=0 . NOM-172-SEMARNAT-2019, (2019). NORMA Oficial Mexicana NOM-172-SEMARNAT-2019, Lineamientos para la obtención y comunicación del Índice de Calidad delAire y Riesgos a la Salud. Retrieved date April 15, 2023, from https://sinaica.inecc.gob.mx/pags/nomsIAS.php#:~:text=El%2018%20 de%20febrero%20de,denominada%20%C3%8Dndice%20AIRE%20Y%20SALUD. PMD, (2011). Plan Municipal de Desarrollo (PMD) del Municipio de Puebla 2011–2014. H. Ayuntamiento de Puebla. 2011. Retrieved date April 20, 2023, from https://www.pueblacapital.gob.mx/vi-planes-municipales-de-desarrollo/174-planes-municipales-de-desarrollo/3687-2011-2014-planes-municipales-de-desarrollo . 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Retrieved date February 20, 2023, from https://calidaddelaire.puebla.gob.mx/views/principal_monitoreo.php . Samet, J. M., Dominici, F., Curriero, F. C., Coursac, I., and Zeger, S. L. (2000). Fine particulate air pollution and mortality in 20 U.S. cities, 1987–1994. N. Engl. J. Med. 343 , 1742–1749. SEDEMA, (2020). Secretaría del Medio Ambiente de la Ciudad de México, SEDEMA. Calidad del aire en la Ciudad de México, Informe 2018. Dirección General de Calidad del Aire, Dirección de Monitoreo de Calidad del Aire. Informe anual 2018. Retrieved date May 5, 2023, from http://www.aire.cdmx.gob.mx/aire/default.php . Shaddicki, G., Thomas, M. L., Jobling, A., Brauer, M., Van Donkelaar, A., Burnett, R., Chang, H. H., Cohen, A., Van Dingenen, R., Dora, C., Gumy, S., Liu, Y., Martin, R., Waller, L. A., West, J., Zidek, J. V. and Prüss-Ustün, A. (2017). Data Integration Model for Air Quality: A Hierarchical Approach to the Global Estimation of Exposures to Ambient Air Pollution. Journal of the Royal Statistical Society Series C: Applied Statistics, Volume 67, Issue 1 , Pages 231–253. https://doi.org/10.1111/rssc.12227 . Shaddick, G., Thomas, M., Amini, H., Broday, D. M., Cohen, A., Frostad, J., Green, A., Gumy, S., Liu, Y., Martin, R.V., Prüss-Üstün, A., Simpson, D., Van Donkelaar, A. and Michael Brauer. (2018). Data integration for the assessment of population exposure to ambient air pollution for Global Burden of Disease Assessment. Environmental Science & Technology, https://doi.org/10.1021/acs.est.8b02864 . SMADSOT, (2021). Secretaría de Medio Ambiente Desarrollo Sustentable y Ordenamiento Territorial (SMADSOT). Informe Anual de Calidad del Aire Zona Metropolitana del Valle de Puebla ZMVP. Del 01 de enero al 31 de diciembre de 2021. Diagnóstico de contaminantes criterio. Partículas PM-10 y PM-2.5, Ozono, Monóxido de Carbono, Bióxido de Azufre y Bióxido de Nitrógeno en la Zona Metropolitana del Valle de Puebla (ZMVP). Subsecretaría de Gestión Ambiental y Sustentabilidad Energética. Dirección de Gestión Calidad del Aire. Departamento de Monitoreo y Evaluación de Emisiones. Retrieved date May 15, 2023, from https://calidaddelaire.puebla.gob.mx/views/reportes_monitoreo.php . SMADSOT, (2023). Secretaría de Medio Ambiente, Desarrollo Sustentable y Ordenamiento Territorial del Gobierno del Estado de Puebla (SMADSOT) Subsecretaria de Gestión Ambiental y Sustentabilidad Energética, Direccción de Gestión de Calidad del Aire. database USB drive. US EPA, (2020). United States Environmental Protection Agency. US EPA. Annual report 2019: Our Nation's Air. Retrieved date May 17, 2023, from https://gispub.epa.gov/air/trendsreport/2020/#home . WHO, (2006). World Health Organization (WHO). Air Quality Guidelines. Global Update 2005. Particulate 631 Matter, Ozone, Nitrogen Dioxide and Sulfur Dioxide; World Health Organization. Retrieved date June 1, 2023, from https://www.who.int/publications/i/item/WHO-SDE-PHE-OEH-06.02 . WWRP/WGNE, (2017). WWRP/WGNE Joint Working Group on Forecast Verification Research. 2017. Forecast Verification methods Across Time and Space Scales. Methods for dichotomous (yes/no) forecasts. Retrieved date April 11, 2023, from https://www.cawcr.gov.au/projects/verification/#Methods_for_dichotomous_forecasts . Additional Declarations No competing interests reported. Supplementary Files SupplementaryinformationEMA.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3775064","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":265411159,"identity":"a6bf2b86-e156-47b9-9b16-b36f8ea16b65","order_by":0,"name":"Javier Omar Castillo-Miranda","email":"data:image/png;base64,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","orcid":"","institution":"Benemérita Universidad Autónoma de Puebla","correspondingAuthor":true,"prefix":"","firstName":"Javier","middleName":"Omar","lastName":"Castillo-Miranda","suffix":""},{"id":265411160,"identity":"cda2d866-3a9f-4b80-9163-267b1f805c16","order_by":1,"name":"José Carlos Mendoza-Hernández","email":"","orcid":"","institution":"Benemérita Universidad Autónoma de Puebla","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"Carlos","lastName":"Mendoza-Hernández","suffix":""},{"id":265411161,"identity":"a0a41caa-2f2a-40b0-9530-3f65217d324a","order_by":2,"name":"José Agustín García-Reynoso","email":"","orcid":"","institution":"Universidad Nacional Autónoma de México","correspondingAuthor":false,"prefix":"","firstName":"José","middleName":"Agustín","lastName":"García-Reynoso","suffix":""},{"id":265411162,"identity":"71871597-2d83-46d7-bba8-2b9ffe89dc8e","order_by":3,"name":"Gabriela Pérez-Osorio","email":"","orcid":"","institution":"Benemérita Universidad Autónoma de Puebla","correspondingAuthor":false,"prefix":"","firstName":"Gabriela","middleName":"","lastName":"Pérez-Osorio","suffix":""}],"badges":[],"createdAt":"2023-12-19 05:44:12","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3775064/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3775064/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49304742,"identity":"dccc469c-23c6-4b7a-8c77-e9faa1cfcf99","added_by":"auto","created_at":"2024-01-08 11:03:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":195764,"visible":true,"origin":"","legend":"\u003cp\u003eGeneral methodological scheme\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource:\u003c/strong\u003e Own elaboration\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3775064/v1/223a1099acd629977e7d6310.png"},{"id":49304743,"identity":"53f33135-f1b9-42a2-9fd8-a08a26bb82f9","added_by":"auto","created_at":"2024-01-08 11:03:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":156630,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of the ZMVP monitoring stations\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSource:\u003c/strong\u003e Own elaboration\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3775064/v1/aab3229aa0ec08a983d94b54.png"},{"id":54097950,"identity":"568bab63-a008-4bd9-8cee-a9d05e7edd1c","added_by":"auto","created_at":"2024-04-04 14:52:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1033350,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3775064/v1/aee3eac0-1140-4b42-8b25-81196f075d0e.pdf"},{"id":49304744,"identity":"c935f2f8-5148-4546-990a-746953a11ad5","added_by":"auto","created_at":"2024-01-08 11:04:00","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":60790,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryinformationEMA.docx","url":"https://assets-eu.researchsquare.com/files/rs-3775064/v1/4dd84c2dc5d24b3c1878aa6d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing Air Quality Forecasting in the Metropolitan Zone of the Valley of Puebla: A Comparative Analysis of CAMS and REMA Data","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eA study on Air Quality in Latin America cities underscores the urgent need of improve the environment implementing public policies. According to the analysis, Brazil had the highest number of premature deaths due to air pollution, with 24 thousand cases in 2008. Mexico followed closely in second place with 15 thousand deaths in the same year. The CAI study also includes the cities of Mexico City, Puebla, Monterrey, Guadalajara, Ciudad Ju\u0026aacute;rez, and Le\u0026oacute;n (CAI, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn 2011, a startling statistic emerged 64% of asthma-related deaths affected children under the age of five years across the country. Furthermore, specific states recorded the highest number of asthma-related deaths in children, with Veracruz with 21 cases, Chiapas and Puebla reporting 16 cases.\u003c/p\u003e \u003cp\u003eAdding to these concerns, the Organization for Economic Cooperation and Development (OECD) predicts that if the same trend in air pollution continues, it will turn into the main reason of early death in the world (REDIM, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study area encompasses the Metropolitan Zone of the Valley of Puebla (MZVP). The municipality of Puebla is situated within this region, located in the central-western part of the State of Puebla. It is positioned at a north latitude of 19\u0026deg;02'38\" and a west longitude of 98\u0026deg;11'50\"; with an elevation of 2,137 meters above sea level. Puebla shares its borders with various regions: to the north, it adjoins the state of Tlaxcala; to the east, it connects with the municipalities of Amozoc, Cuautinch\u0026aacute;n, Tzicatlacoyan and Tepatlaxco de Hidalgo; to the south, it abuts Teopantl\u0026aacute;n and Huehuetl\u0026aacute;n el Grande, while to the west, it shares boundaries with Cuautlancingo, San Pedro Cholula, Ocoyucan and San Andr\u0026eacute;s Cholula. The City Council covers an area of 524.31 km\u003csup\u003e2\u003c/sup\u003e, with a population of 1\u0026rsquo;576,259 people (INEGI, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe air quality in the Metropolitan Zone of the Valley of Puebla faces significant challenges, primarily stemming from the rising number of vehicles. This issue is exacerbated by the absence of sustainable mobility policies aimed at reducing environmental impacts and the limited regulations governing pollutant emissions from local industries.\u003c/p\u003e \u003cp\u003eThe Municipality of Puebla is grappling with severe mobility problems due to unregulated urban expansion and the influence of suburban municipalities. This has led to increased commute times and expenses for residents traveling between their homes, workplaces, and essential services. Consequently, there has been a notable surge in the use of private cars and public transportation.\u003c/p\u003e \u003cp\u003eWhile the Urban Articulated Transport Network (RUTA) represents a viable alternative, the traditional model of individual car ownership still persists. This model suffers from various shortcomings, including long travel distances, delays, accessibility issues, and safety concerns, all of which contribute to road congestion.\u003c/p\u003e \u003cp\u003eThis congestion can be attributed to the staggering growth of the vehicle fleet, which has increased by up to 89.2% as of 2020 compared to the year 2000. This translates to an additional 251,673 vehicles, with an average annual growth rate of 3.24% (PMD, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnother concerning factor impacting air quality in the study area is the degradation of forest ecosystems. This includes illegal logging activities aimed at harvesting carbon and wood theft within the Malinche National Park, in the General L\u0026aacute;zaro C\u0026aacute;rdenas del R\u0026iacute;o State Park, Flor del Bosque. Additionally, extensive livestock farming within the Sierra del Tentzo State Reserve and urban expansion around the Valsequillo Wetland State Park (PMD, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) contribute to ecosystem disruption.\u003c/p\u003e \u003cp\u003eFurthermore, between 1984 and 2018, the municipality witnessed varying degrees of environmental deterioration. Approximately 25.2% of its forested area, equivalent to around 2,800 hectares, suffered damage. Conservation forests also declined from 27\u0026ndash;15.8%, covering roughly 1,689 hectares (INEGI, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eClimate is a factor that also influences air quality. The municipality of Puebla experiences a temperate subhumid climate characterized by a summer rain regime. The speed and direction of the wind also play a crucial role in determining pollutant dispersion. The prevailing winds in the region are oriented from the NNE to SSW with an average velocity of 1.6 m/s. The highest wind speed is typically recorded in January at 2.4 m/s, while the lowest occurs in December, dropping to 0.5 m/s.\u003c/p\u003e \u003cp\u003eThe movement of these winds interacts with the cold mass of the La Malinche volcano, resulting in the formation of cold air currents that affect the entire surface of the Puebla Municipality. The rainy season spans from May to October, with peaks in June and September. Precipitation is distributed almost uniformly across the entire territory. The minimum temperature is recorded at the upper part of the La Malinche volcano, where it can drop to as low as 5\u0026deg;C. Conversely, in the southern zone of the municipality, the average annual temperature reaches a more temperate 18\u0026deg;C (PMD, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe assessment of air pollution in major cities has traditionally relied on ground-based measurements. However, the advancements in satellite remote sensing present a significant opportunity. This technology allows for the establishment of large-scale patterns showcasing the impact of both natural and human-generated emissions on the overall composition of the Earth's atmosphere at a global scale.\u003c/p\u003e \u003cp\u003eFurthermore, satellite monitoring significantly augments the influx of atmospheric data, emphasizing the necessity for global systems to harness this data for comprehensive analysis (Hollingsworth et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1. Atmospheric composition prediction system for the Copernicus Atmospheric Service\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe European Copernicus program was preceded by the Global Environment and Security Monitoring Project (GEMS). Furthermore, the integration of other Monitoring of Atmospheric Composition and Climate (MACC and MACC-II) projects (Hollingsworth et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) made possible the development of a new and innovative prediction system for atmospheric structure, destined for the Copernicus Atmospheric Service and supported by the European Commission. The system carries out global air quality and pollution forecasts. Their predictions constitute warning systems to preserve human health. Also, it predicts exposure to ultraviolet rays, attainable solar energy and dangers occasioned by dust storms. The Copernicus Atmosphere Monitoring Service (CAMS) uses satellites to monitor and forecast aerosols and trace atmospheric gases at a global level. It is uses the combination of satellite vigilance of atmospheric conformation with, last generation of atmospheric models with applications in air pollution (Hollingsworth et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Flemming et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eData assimilation is a method that combines model findings, joining satellite observations and in-situ observations. This technique is employed in numerical weather prediction to acquire a trustworthy and quality basis for the model forecast. For the Copernicus Atmospheric Service, the Integrated Forecast System (IFS) of the European Center for Medium-Range Weather Forecasts (ECMWF). It has been suitable to integrate satellite observations concerning atmospheric composition (Flemming et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and incorporate procedures for aerosol modeling (Morcrette et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Benedetti et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe gathering of distinct inventories of anthropogenic and biogenic emanations on a global and regional scale (Granier et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) constitutes the basis of the estimates of modern regional models such as CHIMERE, EMEP, EURAD, LOTOS, MATCH, MOCAGE, and SILAM. These operate in the European region for the Copernicus Atmospheric Service (Kukkonen et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Therefore, the regional results favor enough users to generate air quality predictions at the urban and street level. An important application is related to the formation of secondary aerosols, which is an important contribution to fine particles (PM2.5) (Flemming et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In the case of North America, the results of the global model that uses a 0.4x0.4 degree mesh are used.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2. Influence of air pollution on health\u003c/h2\u003e \u003cp\u003eAmbient air pollution in 2015 contributed to the global magnitude of morbidity. It is due to the increase in the age of the population in the last 25 years. Weighted average PM\u003csub\u003e2.5\u003c/sub\u003e concentrations with an approximate resolution of 11 km \u0026times; 11 km. These were supported by satellites, chemical transport models and ground-level measurements. Likewise, in 2015, PM\u003csub\u003e2.5\u003c/sub\u003e turned out to be the fifth risk factor for mortality, causing 4.2\u0026nbsp;million deaths and characterizing 7.6% of global deceases (Cohen et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Ambient PM\u003csub\u003e2.5\u003c/sub\u003e is related to 412,000 anticipated deceases due to risk exposure in more than 41 European countries (EEA, 2019).\u003c/p\u003e \u003cp\u003eThe American Cancer Society reports that air pollution in metropolitan areas is a risk factor for heart disease and causes death. Likewise, mortality from long-term exposure to suspended particles (PM) is linked to ischemic heart disease, arrhythmias, heart failure, and cardiac arrest. An increase of 10 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e in PM concentration causes an 8\u0026ndash;18% increase in mortality risk (Pope et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe increase in mortality and morbidity rates in developed and developing countries is associated with air pollution. It has caused air quality standards to become more severe. An evaluation of the effect of PM\u003csub\u003e10\u003c/sub\u003e in 20 of the biggest urban areas in the United States in the period from 1987 to 1994, demonstrate that the concentration level of PM\u003csub\u003e10\u003c/sub\u003e is related to the death rate, for various causes and for cardiovascular and respiratory diseases. That led to a 0.51% increase in the overall mortality rate for every 10 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e increase in PM\u003csub\u003e10\u003c/sub\u003e concentrations. Meanwhile, the predicted growth in the relative ratio of death associated with cardiovascular and respiratory was 0.68% percent when the PM\u003csub\u003e10\u003c/sub\u003e concentration level increased by 10 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e. These findings inform measures to monitor levels of breathable particles in outdoor air (Samet et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2000\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSatellite data support the generation of a model to examine the relative effects on mortality from exposure to PM\u003csub\u003e2.5\u003c/sub\u003e in short periods. The model couples the optical depth of aerosols and measurements at ground level in Massachusetts, United States, from 2000 to 2008. Likewise, the increase in the concentration of PM\u003csub\u003e2.5\u003c/sub\u003e by 10 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, increases mortality by 2.8%. On the other hand, long-term contact showed an increase in the odds ratio (OR) of 1.6, when the level of PM\u003csub\u003e2.5\u003c/sub\u003e increased by 10 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e. Local PM\u003csub\u003e2.5\u003c/sub\u003e presented an OR of 1.4. The methodology used makes it possible to analyze larger areas, even far from urban and rural areas, with better spatial quality (Kloog et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAn assessment in 2016 established that 95% of the globe inhabitants existed in zones where PM\u003csub\u003e2.5\u003c/sub\u003e concentration degree surpass the World Health Organization's (WHO) guideline of an annual average of 10 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (WHO, 2006). This assessment is the product of the agreement between the results of the data integration model for air quality (DIMAQ) and the global population estimate cell by cell (Shaddick, 2018). DIMAQ was carried out in the Bayesian hierarchical modeling (BHM) environment (Shaddick, 2017).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Air pollution in the Metropolitan Zone of the Puebla Valley\u003c/h2\u003e \u003cp\u003eThe Red Estatal de Monitoreo Atmosf\u0026eacute;rico (State Atmospheric Monitoring Network, REMA) is a mechanism that allows reporting on air quality in a given area. It began operating during 2000, with the purpose of measuring atmospheric pollutants and meteorological parameters. As well as determining the influence that these have on the Municipality of Puebla and its metropolitan area. The metropolitan region consists of municipalities: San Pedro Cholula, San Andr\u0026eacute;s Cholula, Cuautlancingo, Coronango and Amozoc (REMA, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe State Atmospheric Monitoring Network; is managed by the Secretariat of Environmental Sustainability and Territorial Planning of Puebla State Government. It is comprises of five continuous monitoring stations: Technological University of Puebla, Agua Santa, Ninfas, Benem\u0026eacute;rito Instituto Normal del Estado located in the Municipality of Puebla and the Velodrome station in the municipality of Coronango. These stations determine the criteria pollutants: carbon monoxide, sulfur dioxide, nitrogen dioxide, ozone and particles with an equivalent aerodynamic diameter equal to or less than 10 micrometers (PM\u003csub\u003e10\u003c/sub\u003e) and 2.5 micrometers (PM\u003csub\u003e2.5\u003c/sub\u003e). Additionally, it integrates the measurement of wind way and speed, temperature and relative humidity (INECC, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAir quality in the Metropolitan Zone of the Valley of Puebla is evaluated by the percentage of days of the year that the regulated limit is exceeded [NOM-025-SSA1-\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)]. PM₁₀ particles exceeded the 24-hour limit on 24% of the days and PM₂.₅ particles exceeded 2.5%, both at the 24-hour limit and the annual average; In Coronango, PM₁₀ particles exceeded the 24-hour limit by 0.4%. During 2018, at least one Environmental Health regulation [NOM-172-SEMARNAT-\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)] was violated on 32% of the days in Puebla and on 0.3% of the days in Coronango (INECC, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe air quality standard in the Municipality of Puebla for PM\u003csub\u003e10\u003c/sub\u003e established in 24 hours [NOM-025-SSA1-\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)], exceeded 72 days in 2019. In the municipality of Coronango with the information available, the limit was exceeded on 19 of the 259 days. In both municipalities, the highest concentrations of PM\u003csub\u003e10\u003c/sub\u003e occurred between January and May. Compared to 2018, a slight decrease was recorded in the City of Puebla and an increase in Coronango in accordance with the 24-hour limit (INECC, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In relation to PM\u003csub\u003e2.5\u003c/sub\u003e in Puebla in 2019, there were 250 days of good air quality, 101 with acceptable standard, 10 with poor quality and 4 with very poor quality, according to [NOM-172-SEMARNAT-\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)]. The highest concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e were also observed between January and May (INECC, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDuring the hot and dry period from February to May 2021, PM\u003csub\u003e10\u003c/sub\u003e reached levels of concentration on 59 days that exceeded the maximum permitted limit. It is in accordance with the provisions of the regulations on particles [NOM-025-SSA1-\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)]. On the other hand, the warm-humid season from June to October decreased the magnitude of PM\u003csub\u003e10\u003c/sub\u003e concentrations. Meanwhile, PM\u003csub\u003e2.5\u003c/sub\u003e showed that the maximum permissible limit was exceeded in three days [NOM-025-SSA1-\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)]. Air quality was mainly recorded as fair (SMADSOT, 2021).\u003c/p\u003e \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e is formed naturally through volcanic emissions, forest fires, soil dust and bioaerosols. They can also originate from human activities such as vehicle combustion and the construction industry (SEDEMA, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). PM\u003csub\u003e10\u003c/sub\u003e affects health directly, causing lung diseases such as asthma and chronic obstructive pulmonary disease (COPD) (MacNee and Donalson, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2000\u003c/span\u003e). Decreased visibility, in addition to the impact of nutrients in soils, bodies of water, and forests due to deposition processes, are the environmental consequences that PM\u003csub\u003e10\u003c/sub\u003e can cause in the environment (U.S. EPA, 2020).\u003c/p\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e contains a smaller aerodynamic diameter than PM\u003csub\u003e10\u003c/sub\u003e and is part of the latter, contributing around 52% to its total mass. Environmental PM\u003csub\u003e2.5\u003c/sub\u003e affects the health of the population, harming the cardiovascular system. It includes heart attacks and strokes. It also damages the respiratory system by causing asthma attacks and cancer. It can have consequences such as hospitalizations, visits to the emergency room and possibly premature death (SEDEMA, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"2. Materials and methods","content":"\u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the methodology employed to acquire data, generate contingency tables, and derive results.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Air quality data\u003c/h2\u003e \u003cp\u003eThe process commences within the Atmosphere Data Store (ADS), serving as the primary gateway for data sourced from the Copernicus Atmospheric Monitoring Service (CAMS). Within this system, access is granted to the CAMS global atmospheric composition forecasts section. For this analysis, the Particulate Matter variables, namely PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e, are considered at a single level. The temporal scope spans from December 31, 2020, to December 31, 2021, with daily downloads scheduled at 00:00 UTC, spanning a delivery window from 0 to 23 hours.\u003c/p\u003e \u003cp\u003eThe Download Type selected is CAMS forecast, and the geographic area of interest corresponds to a restricted zone defined by specific geographic coordinates encompassing the Metropolitan Zone of the Valley of Puebla. The data retrieved is formatted in netCDF (CAMS, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo process this data, a Python script is employed. This script extracts PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e values in kg/m\u0026sup3; and converts them to \u0026micro;g/m\u0026sup3; using the nearest neighbor method based on the coordinates of the monitoring stations.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 CAMS Forecast and the Air and Health Index Database\u003c/h2\u003e \u003cp\u003eThe Air and Health Indices (AHI) within the CAMS Copernicus forecast are calculated following the procedure described in the standard [NOM-172-SEMARNAT-\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)]. A Python script is employed for this purpose, resulting in the generation of 8,760 AHI records per station. The CAMS forecast Air and Health Index database comprises a total of 43,800 records, corresponding to the estimated values for the five stations of the Monitoring Network (REMA).\u003c/p\u003e \u003cp\u003eThe concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e provided by the CAMS forecast are define within a grid with approximate dimensions of 40x40 km, whereas the measurements from the stations are point-specific. This discrepancy introduces a systematic error, as CAMS concentrations tend to underestimate observations from surface monitoring stations. To rectify this error, scaling factors are calculated through difference and ratio method to obtain an adjusted CAMS value for the Air and Health Index.\u003c/p\u003e \u003cp\u003eThe procedure includes the extraction of maximum daily AHI values from both CAMS and REMA for each month of the study year. The difference scaling factor entails subtracting the REMA AHI from the CAMS AHI on a daily basis and obtaining a monthly average value. This data is then added to the daily CAMS AHI to determine the difference-adjusted CAMS value. The ratio scaling factor, on the other hand, involves dividing the REMA AHI by the CAMS AHI for each day and obtaining a monthly average value. This value is then multiplied by the daily CAMS AHI to arrive at the ratio-adjusted CAMS value.\u003c/p\u003e \u003cp\u003eSubsequently, a comparison of these scaling factors is carried out considering both difference and ratio methods. This comparison yields difference- and ratio-adjusted CAMS values, which are used to predict the occurrence of events characterized by poor air quality, based on the Air and Health Index (AHI) observed. Specifically, the AHI is deemed to be greater than 45 (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) for PM\u003csub\u003e2.5\u003c/sub\u003e and greater than 75 (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) for PM\u003csub\u003e10\u003c/sub\u003e, as per the 12-hour weighted moving average interval [NOM-172-SEMARNAT-\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)]. This comprehensive procedure allows us to determine which scaling factor offers the best fit for predicting poor air quality events at the stations of the Atmospheric Monitoring Network.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Air and Health Indices Database of measurements at ground level\u003c/h2\u003e \u003cp\u003eThe database comprises average hourly measurements of PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e, obtained from monitoring stations within the Automatic Air Quality Monitoring Network (SMADSOT, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Please refer to Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e for sample names, acronyms, and geographic coordinates, and for a visual representation of their geographic locations, consult Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMonitoring stations of the Metropolitan Zone of the Valley of Puebla\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026minus;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAcronyms\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoordinates (Latitude, Longitude)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgua Santa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSTA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e18.9874, -98.2497\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBenem\u0026eacute;rito Instituto Normal del Estado\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBINE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e19.0673, -98.2245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParque de las Ninfas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNINFAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e19.0413, -98.2142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversidad Tecnol\u0026oacute;gica de Puebla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e19.0567, -98.1517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVel\u0026oacute;dromo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVELODROMO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c4\"\u003e \u003cp\u003e19.1158, -98.2776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe average hourly measurements of PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e establish the Air and Health Index (AHI) database for PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e corresponding to the year 2021. It can be consulted on the official page of the State Network of Atmospheric Monitoring (REMA) (REMA, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The AHI database for PM\u003csub\u003e2.5\u003c/sub\u003e integrates: 4,045 records for Agua Santa Station (STA), 4,567 records for BINE Station, 7,200 records for NINFAS station, 6,822 records for UTP station and 0 records for VELODROME station. The AHI database for PM10 contains: 4,680 records for Agua Santa Station (STA), 7,622 records for BINE Station, 7,758 records for NINFAS station, 6,982 records for UTP station and 1,135 records for VELODROME station.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Procedures for dichotomous forecasts\u003c/h2\u003e \u003cp\u003eThe Persistence forecast serves as a baseline for comparison with the CAMS forecast, enabling us to assess its performance. We utilize 2x2 contingency tables to categorize days of the year into those that exceed or do not exceed the Air and Health Index across three data groups: Measurements, the Persistence forecast, and the CAMS forecast.\u003c/p\u003e \u003cp\u003eDichotomous statistics ensure that events are classified into two categories: \"yes, the event occurs\" or \"no, the event does not occur.\" To evaluate this type of forecast, we employ 2x2 contingency tables. These tables comprise four cells, representing the following categories for a Persistence forecast: Successes (forecasted yes, observed yes), failures (forecasted no, observed no), false alarms (forecasted yes, observed no), and correct negatives (forecasted no, observed no). The total counts of observed and predicted events and non-events are located along the bottom and right-hand corner of the contingency table, which is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and referred to as \"marginal distribution (WWRP/WGNE, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Gold et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eContingency table\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eObserved\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eForecast\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eHits\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eFalse alarms\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eForecast yes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMisses\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eCorrect negatives\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eForecast no\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eObserved yes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eobserved no\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe contingency table serves as a valuable tool for pinpointing the types of errors in forecasting. An ideal forecasting method should yield only hits and correct negatives, with no misses or false alarms. By combining elements from the contingency table, we can derive dichotomous statistics that provide insights into various aspects of forecast performance. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays the optimal values of these dichotomous statistics, which are described below.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003ePrecision (fraction correct)\u003c/strong\u003e \u003cp\u003eIndicates what portion of the forecasts were correct.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eAccuracy = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{hits + correct negatives}{total}\\)\u003c/span\u003e\u003c/span\u003e (1)\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBias score (frequency bias)\u003c/strong\u003e \u003cp\u003eCompares the frequency of the predicted events and the frequency of the observed events. Determines whether there is a tendency to under forecast (BIAS\u0026thinsp;\u0026lt;\u0026thinsp;1) or over forecast (BIAS\u0026thinsp;\u0026gt;\u0026thinsp;1) events.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eBIAS = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{hits + false alarms}{hits + misses}\\)\u003c/span\u003e\u003c/span\u003e (2)\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eDetection probability (hit rate)\u003c/strong\u003e \u003cp\u003eIndicates the fraction of observed \"yes\" events that were correctly predicted.\u003c/p\u003e \u003c/p\u003e \u003cp\u003ePOD = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{hits}{hits + misses}\\)\u003c/span\u003e\u003c/span\u003e (3)\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFalse alarm rate\u003c/strong\u003e \u003cp\u003eExpresses the fraction of the forecast events that will occur, but did not occur.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eFAR = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{false alarms}{hits+false alarms}\\)\u003c/span\u003e\u003c/span\u003e (4)\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eProbability of false detection (false alarm ratio)\u003c/strong\u003e \u003cp\u003eConsiders the part of events not observed; were incorrectly predicted as \u0026ldquo;yes.\u0026rdquo;\u003c/p\u003e \u003c/p\u003e \u003cp\u003ePOFD = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{false alarms}{correct negatives + false alarms}\\)\u003c/span\u003e\u003c/span\u003e (5)\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSuccess Ratio\u003c/strong\u003e \u003cp\u003eConsiders the fraction of events predicted \u0026ldquo;yes\u0026rdquo; that were correctly observed.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eSR = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{hits}{hits+false alarms}\\)\u003c/span\u003e\u003c/span\u003e (6)\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eThreat score (critical success rate)\u003c/strong\u003e \u003cp\u003eDetermines the part of observed and/or predicted events that were suitably predicted.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eTS = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{hits}{hits+misses+false alarms}\\)\u003c/span\u003e\u003c/span\u003e (7)\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEquitable threat score (Gilbert skill score)\u003c/strong\u003e \u003cp\u003eQuantifies the portion of observed and/or predicted episodes that were suitably predicted, coupled by hits related to aleatory probability.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eETS = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{{hits-hits}_{random}}{{hits + misses+false alarms-hits }_{random}}\\)\u003c/span\u003e\u003c/span\u003e (8)\u003c/p\u003e \u003cp\u003ewhere:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$${hits}_{random }= \\frac{(hits+misses)(hits+false alarms)}{total}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHanssen and Kuipers differentiate (true ability statistic, Peirce skill score)\u003c/strong\u003e \u003cp\u003eIndicates the success of the forecast separating \u0026ldquo;yes\u0026rdquo; events from \u0026ldquo;no\u0026rdquo; events.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eHK = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{hits}{hits+misses}\\)\u003c/span\u003e\u003c/span\u003e - \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{false alarms}{false alarms + correct negatives}\\)\u003c/span\u003e\u003c/span\u003e (9)\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHeidke skill score (Cohen's K)\u003c/strong\u003e \u003cp\u003eEstablishes the share of correct predictions afterward removing those forecast that would be correct due to chance alone.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eHSS = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{{\\left(hits+correct negatives\\right)-\\left(expected correct\\right)}_{random}}{{N-\\left(expected correct\\right)}_{random}}\\)\u003c/span\u003e\u003c/span\u003e (10)\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\({\\left(expected correct\\right)}_{random} = \\frac{1}{N}[\\left(hits+misses\\right)\\left(hits+false alarms\\right)+(correct negatives+misses\\left)\\right(correc negatives+false alarms)\\)\u003c/span\u003e \u003c/span\u003e]\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eOdds Ratio Skill Score (Yule\u0026rsquo;s Q)\u003c/strong\u003e \u003cp\u003eIndicates how far the forecast was ahead of chance.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eORSS = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\frac{hits * correct negatives - misses *false alarms}{hits * correct negatives + misses *false alarms}\\)\u003c/span\u003e\u003c/span\u003e (11)\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the optimal values of the dichotomous statistics.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMetric, range and associated optimal values\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetric\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOptimal value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 to 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBIAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 to \u0026infin;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 to 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 to 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOFD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 to 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 to 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 to 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eETS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1/3 to 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1 to 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1 to 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eORSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1 to 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Evaluation of the CAMS forecast using Air and Health Indices based on ground measurements\u003c/h2\u003e \u003cp\u003eIt were perform a dichotomous evaluation using the databases of the Air and Health Indices (AHI) for PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e, incorporating data from the Monitoring Network measurements and the CAMS forecast model. The dichotomous verification is based on the following premise: it assesses whether the Integrated Air Quality Index (AHI) exceeds 45 for PM\u003csub\u003e2.5\u003c/sub\u003e and 75 for PM\u003csub\u003e10\u003c/sub\u003e. This evaluation results in a 'yes' or 'no,' 'true' or 'false' determination for both the observed and modeled values.\u003c/p\u003e \u003cp\u003eThe AHI indicates that air quality is considered poor and poses a high risk when the 12-hour weighted moving average surpasses 45 (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) for PM\u003csub\u003e2.5\u003c/sub\u003e and 75 (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) for PM\u003csub\u003e10\u003c/sub\u003e, in accordance with [NOM-172-SEMARNAT-\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)].\u003c/p\u003e \u003cp\u003eNext, a 2x2 contingency table is constructed to identify the days of the year when observations either exceed or do not exceed the AHI, as well as the days when CAMS forecasts exceed or do not exceed the AHI. These four values are then used to derive the dichotomous statistics, which are described previously.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Comparison of the REMA persistence forecast with the CAMS forecast\u003c/h2\u003e \u003cp\u003eThe comparison between the REMA AHI persistence forecast and the CAMS AHI forecast adjusted by ratio allows us to assess the performance of CAMS relative to the REMA Persistence forecast. This evaluation is presented in Tables \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e reveals that the adjustment using the CAMS ratio for PM\u003csub\u003e2.5\u003c/sub\u003e results in a higher number of events where the AHI exceeds 45 \u0026micro;g/m\u0026sup3; at the Agua Santa, Ninfas, and UTP stations. However, the Persistence forecast outperforms CAMS only at the BINE station within the Atmospheric Monitoring Network.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eREMA Persistence Forecast and CAMS Forecast Adjusted by PM\u003csub\u003e2.5\u003c/sub\u003e Ratio\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eREMA PM\u003csub\u003e2.5\u003c/sub\u003e Persistence Forecast\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e \u003cp\u003eCAMS PM\u003csub\u003e2.5\u003c/sub\u003e forecast Ratio adjustment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eNo. of times AHI\u0026thinsp;\u0026gt;\u0026thinsp;45 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e \u003cp\u003eNo. of times AHI\u0026thinsp;\u0026gt;\u0026thinsp;45 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgua Santa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBINE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNinfas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVelodromo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAgua Santa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBINE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNinfas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eUTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eVelodromo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJanuary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFebruary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApril\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJune\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJuly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAugust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOctober\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNovember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows that the adjustment by CAMS ratio for PM\u003csub\u003e10\u003c/sub\u003e includes a greater number of times that the AHI is greater than 75 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e at the Agua Santa and BINE stations. The Persistence forecast is higher at the UTP and Velodromo stations and with a similar value at the Ninfas station of the Atmospheric Monitoring Network.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eREMA Persistence forecast and CAMS forecast adjusted by PM\u003csub\u003e10\u003c/sub\u003e ratio\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eREMA PM\u003csub\u003e10\u003c/sub\u003e Persistence Forecast\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e \u003cp\u003eCAMS PM\u003csub\u003e10\u003c/sub\u003e forecast Ratio adjustment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eNo. of times AHI\u0026thinsp;\u0026gt;\u0026thinsp;75 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c11\" namest=\"c7\"\u003e \u003cp\u003eNo. of times AHI\u0026thinsp;\u0026gt;\u0026thinsp;75 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAgua Santa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBINE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNinfas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eUTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVelodromo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAgua Santa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBINE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNinfas\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eUTP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eVelodromo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJanuary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFebruary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApril\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJune\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJuly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAugust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOctober\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNovember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Comparison of dichotomous statistics of Copernicus CAMS and REMA Persistence\u003c/h2\u003e \u003cp\u003eDichotomous statistics describe characteristics of forecast performance. These were obtained from the Air and Health Indices of [PM\u003csub\u003e10\u003c/sub\u003e] and [PM\u003csub\u003e2.5\u003c/sub\u003e] of Copernicus CAMS adjusted by ratio and from the Air and Health Indices of [PM\u003csub\u003e10\u003c/sub\u003e] and [PM\u003csub\u003e2.5\u003c/sub\u003e] of REMA in the ZMVP.\u003c/p\u003e \u003cp\u003eThe evaluation of dichotomous statistics is carried out according to the results available in tables A5, A6, A7, A8, A9, A10, A11, A12 and A13 contained in \u003cb\u003esupplementary information\u003c/b\u003e. The purpose is to establish the performance of CAMS with respect to the REMA Persistence Forecast.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e represents the percentage proportion of values that register monthly the dichotomous statistics at the monitoring stations with respect to PM\u003csub\u003e2.5\u003c/sub\u003e. The percentage proportion indicates the number of hits close to or equal to the optimal value of the statistic in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The comparison considers when CAMS is better than Persistence, CAMS is the same as Persistence, and Persistence is better than CAMS. The results indicate a higher number of statistics with a higher percentage when CAMS equals Persistence: ACCURACY, POD, FAR POFD, SR, TS and ORSS. Coincidence in the percentage value in the ETS and HSS statistics when CAMS is better than persistence, and Persistence is better than CAMS. Persistence is better than CAMS for BIAS and HK statistics.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePercentage comparison of dichotomous statistics of Copernicus CAMS and REMA Persistence adjusted by ratio for PM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"11\" nameend=\"c12\" namest=\"c2\"\u003e \u003cp\u003ePerformance of Copernicus CAMS ratio adjustment versus REMA persistence forecast for PM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACCURACY (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBIAS (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePOD (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFAR (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePOFD (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSR (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTS (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eETS (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHK (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eHSS (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eORSS (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAMS better than Persistence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e6.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e18.75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e16.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e20.83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAMS equal than Persistence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e37.50\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e27.08\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e16.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e41.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e16.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e25.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e6.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e14.58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersistence better than CAMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e35.42\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e18.75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e18.75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e20.83\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e12.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e reveal the network stations with the best monthly value of dichotomous statistics. It stands out that in March the UTP and BINE stations coincide in the FAR, SR, TS and HSS statistics. While in the month of July, UTP and BINE agree on POD, FAR, SR, TS and ORSS. In the month of August and September, the Agua Santa, BINE, and Ninfas stations coincide in the ACCURACY and POFD statistics. In general, the stations with the highest number of hits are UTP with 45 and BINE with 24.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of stations with the best percentage of PM\u003csub\u003e2.5\u003c/sub\u003e CAMS dichotomous statistic value\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"11\" nameend=\"c12\" namest=\"c2\"\u003e \u003cp\u003eStations with the best percentage value of dichotomic statistics of PM\u003csub\u003e2.5\u003c/sub\u003e CAMS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACCURACY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBIAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePOD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePOFD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eETS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eHSS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eORSS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJanuary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA, U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFebruary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA, U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eU, B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eU, B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eU, B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eU, B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eApril\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eU, N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJune\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA, N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA, N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJuly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA, N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eU, B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eU, B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA, U, N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eU, B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eU, B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eU, B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAugust\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA, N, B, U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA, N, B, U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSeptember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA, N, B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA, B, N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOctober\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA, N, U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eU, N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eB, A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNovember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN, U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eU, N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eB, A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecember\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eU, N, A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eU, N\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eU\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003eA\u0026thinsp;=\u0026thinsp;AGUA SANTA B\u0026thinsp;=\u0026thinsp;BINE N\u0026thinsp;=\u0026thinsp;NINFAS U\u0026thinsp;=\u0026thinsp;UTP\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e represents the percentage proportion of values that the dichotomous statistics record monthly at the monitoring stations with respect to PM\u003csub\u003e10\u003c/sub\u003e. The comparison considers when CAMS is better than Persistence, CAMS is the same as Persistence, and Persistence is better than CAMS. The results indicate for all statistics a higher percentage when Persistence is better CAMS.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePercentage comparison of dichotomous statistics of Copernicus CAMS and REMA Persistence adjusted by ratio for PM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"11\" nameend=\"c12\" namest=\"c2\"\u003e \u003cp\u003ePerformance of Copernicus CAMS ratio adjustment versus REMA persistence forecast for PM\u003csub\u003e10\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACCURACY (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBIAS (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePOD (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFAR (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePOFD (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSR (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eTS (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eETS (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHK (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eHSS (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eORSS (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAMS better than Persistence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e18.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e26.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e26.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e18.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAMS equal than Persistence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePersistence better than CAMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e53.33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e73.33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e40.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e40.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e48.33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e38.33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e48.33\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e51.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e51.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e51.67\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cb\u003e40.00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003eIn conclusion, our study presents a substantial step forward in air quality forecasting within the Metropolitan Zone of the Valley of Puebla. We have successfully implemented a procedure that effectively addresses systematic errors in data retrieval from the Copernicus CAMS forecast, enabling us to make precise predictions of poor air quality events in alignment with the standards set forth in NOM-172-SEMARNAT-2019.\u003c/p\u003e \u003cp\u003eOur findings clearly demonstrate that the adjustment by ratio, applied to both PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e, significantly enhances the forecasting performance of CAMS. This adjustment surpasses the REMA persistence forecast for some periods and stations in accurately predicting unsatisfactory air quality events attributed to PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003eAdditionally, the monthly percentage comparison of dichotomous statistics reveals nuanced insights. While Persistence outperforms CAMS in most cases for both PM\u003csub\u003e2.5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e, several specific statistics, including ACCURACY, POD, FAR, POFD, SR, TS, ETS, HSS, and ORSS, can be employed for a preliminary evaluation of CAMS PM\u003csub\u003e2.5\u003c/sub\u003e forecasting performance at the stations of the State Atmospheric Monitoring Network. Notably, these statistics demonstrate acceptable percentages.\u003c/p\u003e \u003cp\u003eFurther analysis of PM\u003csub\u003e2.5\u003c/sub\u003e forecasts from the UTP and BINE stations as diagnostic indicators of poor-quality ambient PM\u003csub\u003e2.5\u003c/sub\u003e events indicates that they likely represent regional issues. These stations exhibit the highest number of correct hits in the dichotomous statistics throughout the year. Conversely, the Agua Santa and Ninfas stations appear to be more representative of local issues, potentially influenced by local emissions, roads, and industrial factors.\u003c/p\u003e \u003cp\u003eWe acknowledge that the non-operational status of certain REMA stations for extended periods resulted in insufficient data for the study. This led to a shortage of PM\u003csub\u003e2.5\u003c/sub\u003e data by 48.38% and PM\u003csub\u003e10\u003c/sub\u003e data by 37.12%, limiting the comprehensiveness of our analysis.\u003c/p\u003e \u003cp\u003eThe significance of our study lies in its pioneering approach. It marks the first time that such a comprehensive comparison has been conducted on the American Continent, specifically in Mexico. These findings provide critical insights and suggest avenues for further improvement, including enhancing resolution, incorporating detailed emissions inventories, and accounting for local meteorological variables.\u003c/p\u003e \u003cp\u003eOur research lays the foundation for enhanced air quality forecasting in the region, offering valuable information for policy decisions and further studies aimed at improving air quality in the Metropolitan Zone of the Valley of Puebla.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJavier Omar Castillo-Miranda expresses gratitude to CONSEJO NACIONAL DE HUMANIDADES CIENCIAS Y TECNOLOG\u0026Iacute;AS (CONAHCYT) for the postdoctoral fellowship that supported this study. Additionally, both Javier Omar Castillo-Miranda and Jos\u0026eacute; Carlos Mendoza-Hern\u0026aacute;ndez extend their thanks to the College of Environmental Engineering within the Faculty of Chemical Engineering at Benem\u0026eacute;rita Universidad Aut\u0026oacute;noma de Puebla. Thanks are also extended to the Air Quality Management Directorate of the Ministry of the Environment, Sustainable Development and Territorial Planning of the Government of Puebla, for providing valuable information for this project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJavier Omar Castillo-Miranda: Formulation of aims, design of methodology, application of statistical to analyze study data, presentation of the data, writing the initial draft. Jos\u0026eacute; Carlos Mendoza-Hern\u0026aacute;ndez: Activity planning and execution, provision of study materials and computing resources, preparation of the published work. Jos\u0026eacute; Agust\u0026iacute;n Garc\u0026iacute;a-Reynoso: Programming and designing computer programs, verification of research results. Gabriela P\u0026eacute;rez-Osorio: Management and coordination for the research activity planning and execution.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed in this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read, understood, and have complied as applicable with the statement on\u0026nbsp;\u0026ldquo;Ethical responsibilities of Authors\u0026rdquo; as found in the Instructions for Authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding was obtained for this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBenedetti, A., Morcrette, J.-J., Boucher, O., Dethof, A., Engelen, R. 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The automatic air quality monitoring system in Puebla measures PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e at five stations in the Puebla and Coronango municipalities. These measurements allow for the determination of the Air and Health Index according to the NOM-172-SEMARNAT-2019 standard for these pollutants. The advancement of satellite monitoring techniques represents an opportunity for air quality management where terrestrial measurements are scarce. However, to obtain reliable data, it is necessary to validate the satellite data with ground measurements from georeferenced monitoring stations. The operational implementation forecast of the Copernicus Atmospheric Monitoring Service (CAMS) allows for conducting out atmospheric pollution exploration processes. An analysis of this forecast data determined that the Persistence forecast is better than the CAMS forecast overall for both PM\u003csub\u003e10\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e. However, the CAMS forecast can be employment for a preliminary evaluation in the prediction of PM\u003csub\u003e2.5\u003c/sub\u003e due to the success in the comparison criteria of the dichotomous statistics ACCURACY, POD, FAR, POFD, SR, TS, ETS, HSS and ORSS.\u003c/p\u003e","manuscriptTitle":"Enhancing Air Quality Forecasting in the Metropolitan Zone of the Valley of Puebla: A Comparative Analysis of CAMS and REMA Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-08 11:03:54","doi":"10.21203/rs.3.rs-3775064/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"92490890-0fd9-432e-9b15-fab66ecebae2","owner":[],"postedDate":"January 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-04T14:43:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-08 11:03:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3775064","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3775064","identity":"rs-3775064","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}