IELTS Reading: Thành Phố Thông Minh Giảm Ô Nhiễm Đô Thị – Đề Thi Mẫu Có Đáp Án Chi Tiết

Mở Bài

Chủ đề về thành phố thông minh và ô nhiễm đô thị đang ngày càng trở nên phổ biến trong các kỳ thi IELTS Reading gần đây. Đây là một trong những chủ đề thuộc nhóm Environment & Technology, xuất hiện thường xuyên với tần suất khoảng 15-20% trong các đề thi thực tế. Sự kết hợp giữa vấn đề môi trường cấp thiết và giải pháp công nghệ hiện đại khiến chủ đề này vô cùng hấp dẫn và có tính ứng dụng cao.

Trong bài viết này, bạn sẽ được trải nghiệm một bộ đề thi IELTS Reading hoàn chỉnh với 3 passages từ dễ đến khó, bao gồm 40 câu hỏi đa dạng hoàn toàn giống với format thi thật. Bạn sẽ học được cách xử lý các dạng câu hỏi phổ biến như True/False/Not Given, Multiple Choice, Matching Headings, và Summary Completion. Đặc biệt, phần đáp án chi tiết kèm giải thích sẽ giúp bạn hiểu rõ cách paraphrase và định vị thông tin chính xác trong bài đọc.

Đề thi này phù hợp cho học viên từ band 5.0 trở lên, giúp bạn làm quen với độ khó tăng dần và rèn luyện kỹ năng quản lý thời gian hiệu quả. Hãy chuẩn bị 60 phút để hoàn thành bài thi và tự đánh giá trình độ của mình một cách chính xác nhất.

Hướng Dẫn Làm Bài IELTS Reading

Tổng Quan Về IELTS Reading Test

IELTS Reading Test là một phần thi đòi hỏi sự tập trung cao độ và khả năng quản lý thời gian hiệu quả. Bạn sẽ có 60 phút để hoàn thành 3 passages với tổng cộng 40 câu hỏi. Không có thời gian thêm để chuyển đáp án sang phiếu trả lời, do đó bạn cần cân đối thời gian hợp lý.

Phân bổ thời gian khuyến nghị:

• Passage 1: 15-17 phút (độ khó Easy, 13 câu hỏi)
• Passage 2: 18-20 phút (độ khó Medium, 13 câu hỏi)
• Passage 3: 23-25 phút (độ khó Hard, 14 câu hỏi)

Lưu ý rằng độ khó tăng dần từ Passage 1 đến Passage 3, vì vậy đừng dành quá nhiều thời gian cho phần đầu và bị thiếu thời gian cho phần cuối. Mỗi câu trả lời đúng được tính 1 điểm, không có điểm âm cho câu sai.

Các Dạng Câu Hỏi Trong Đề Này

Đề thi mẫu này bao gồm 7 dạng câu hỏi phổ biến nhất trong IELTS Reading:

1. Multiple Choice – Câu hỏi trắc nghiệm với 3-4 lựa chọn
2. True/False/Not Given – Xác định thông tin đúng, sai hoặc không được đề cập
3. Matching Information – Nối thông tin với đoạn văn tương ứng
4. Matching Headings – Chọn tiêu đề phù hợp cho các đoạn văn
5. Summary Completion – Điền từ vào chỗ trống trong đoạn tóm tắt
6. Matching Features – Nối đặc điểm với người/địa điểm/sự vật
7. Short-answer Questions – Trả lời câu hỏi bằng từ ngắn gọn

Mỗi dạng câu hỏi đòi hỏi kỹ năng đọc hiểu khác nhau, từ scanning tìm thông tin cụ thể đến skimming nắm ý chính đoạn văn.

IELTS Reading Practice Test

PASSAGE 1 – Smart Cities: A New Approach to Urban Living

Độ khó: Easy (Band 5.0-6.5)

Thời gian đề xuất: 15-17 phút

The concept of smart cities has emerged as one of the most promising solutions to the environmental challenges facing modern urban areas. As populations continue to grow and more people migrate to cities, the strain on infrastructure, resources, and the environment becomes increasingly severe. Smart cities use digital technology and data analytics to improve the quality and performance of urban services, reduce costs and resource consumption, and engage more effectively with citizens.

In Singapore, often cited as a leading example of smart city development, the government has implemented an extensive network of sensors and monitoring devices throughout the city. These devices collect real-time data on everything from traffic flow to air quality, enabling authorities to respond quickly to problems before they escalate. For instance, when sensors detect unusually high levels of particulate matter in a particular neighborhood, the system can automatically alert environmental teams and adjust traffic patterns to reduce vehicle emissions in that area.

Barcelona has taken a different but equally effective approach to becoming a smart city. The Spanish city has focused heavily on public transportation and renewable energy integration. The introduction of smart bus stops equipped with solar panels provides real-time information to passengers while generating clean energy. Additionally, Barcelona’s smart parking system uses sensors to guide drivers to available parking spaces, significantly reducing the time vehicles spend circling blocks looking for parking – a major contributor to urban air pollution.

One of the most innovative aspects of smart city technology is its ability to predict and prevent pollution problems rather than simply reacting to them. In Copenhagen, Denmark, city planners use predictive algorithms to forecast air quality levels up to 48 hours in advance. This allows authorities to implement preventive measures such as temporarily increasing public transit frequency, encouraging remote work, or restricting certain industrial activities during predicted high-pollution periods.

Smart street lighting represents another significant innovation in reducing urban pollution. Traditional street lights consume vast amounts of electricity, much of it generated from fossil fuels. Cities like Los Angeles have replaced millions of conventional street lights with LED fixtures controlled by intelligent systems. These lights automatically adjust their brightness based on factors such as time of day, weather conditions, and pedestrian traffic, reducing energy consumption by up to 70%. This not only cuts electricity costs but also dramatically reduces the carbon footprint associated with urban lighting.

The Internet of Things (IoT) plays a crucial role in smart city pollution reduction. In Amsterdam, a network of connected devices monitors everything from water quality in canals to noise levels in residential areas. Citizens can access this data through mobile apps, making them active participants in environmental protection. When the system detects unusual pollution spikes, it can automatically send alerts to residents, advising them to close windows, avoid outdoor exercise, or use alternative routes.

Waste management has also been revolutionized by smart city technologies. In Seoul, South Korea, the city has implemented a comprehensive smart waste system that includes underground vacuum tubes to transport waste, eliminating the need for numerous garbage trucks that contribute to both air pollution and traffic congestion. The system uses sensors to monitor waste levels in collection points and optimizes collection routes, ensuring trucks only travel when necessary.

However, the success of smart cities in reducing pollution depends heavily on citizen engagement and behavior change. Technology alone cannot solve pollution problems if residents do not adapt their lifestyles accordingly. Many smart cities have therefore invested in educational programs and incentive schemes to encourage environmentally friendly behaviors. For example, Milan offers reduced public transport fares to citizens who regularly choose buses and trains over private cars, while also providing real-time information about the environmental impact of different transportation choices.

Questions 1-13

Questions 1-5: Multiple Choice

Choose the correct letter, A, B, C, or D.

1. According to the passage, smart cities primarily use technology to
A. increase population density
B. improve urban services and reduce resource use
C. replace traditional infrastructure completely
D. eliminate all forms of pollution

2. Singapore’s smart city system uses sensors to
A. monitor only traffic conditions
B. collect data exclusively about air quality
C. gather real-time information on various urban conditions
D. control citizen behavior

3. Barcelona’s smart bus stops are notable because they
A. are powered entirely by fossil fuels
B. provide information and generate renewable energy
C. only display arrival times
D. replace all traditional bus services

4. Copenhagen’s predictive algorithms can forecast air quality
A. one week in advance
B. 24 hours ahead
C. up to 48 hours beforehand
D. in real-time only

5. Los Angeles reduced street lighting energy consumption by replacing lights with
A. solar panels
B. wind turbines
C. LED fixtures with intelligent controls
D. motion-sensor lights only

Questions 6-9: True/False/Not Given

Write TRUE if the statement agrees with the information, FALSE if the statement contradicts the information, or NOT GIVEN if there is no information on this.

6. Smart city technology can only react to pollution after it occurs.

7. Amsterdam’s citizens can access environmental data through mobile applications.

8. Seoul’s smart waste system completely eliminates the need for garbage trucks.

9. Milan charges higher public transport fares to encourage private car use.

Questions 10-13: Matching Information

Match the following statements (10-13) with the correct city (A-E).

A. Singapore
B. Barcelona
C. Copenhagen
D. Los Angeles
E. Amsterdam

10. This city uses algorithms to predict future air quality levels.

11. This city has implemented smart parking to reduce traffic congestion.

12. This city provides citizens with real-time pollution data through apps.

13. This city replaced street lights to reduce carbon emissions significantly.

---

PASSAGE 2 – The Technology Behind Smart City Pollution Control

Độ khó: Medium (Band 6.0-7.5)

Thời gian đề xuất: 18-20 phút

The transformation of traditional cities into smart, environmentally conscious urban centers requires a sophisticated technological infrastructure that goes far beyond simple monitoring devices. At the heart of this transformation lies an integrated network of sensors, communication systems, and artificial intelligence that work together to create a responsive and adaptive urban environment capable of minimizing pollution while maintaining the quality of life for residents.

A. The Sensor Revolution

Modern smart cities deploy thousands of low-cost, high-precision sensors throughout their urban landscape. These devices, often no larger than a mobile phone, can detect minute changes in air composition, measuring pollutants such as nitrogen dioxide, sulfur dioxide, carbon monoxide, and particulate matter as small as 2.5 micrometers (PM2.5). Unlike traditional monitoring stations, which are expensive and limited in number, these new sensors can be installed at street level, on buildings, inside public transportation, and even on mobile platforms such as buses and delivery vehicles, creating a comprehensive, three-dimensional map of air quality across the entire city.

The data collected by these sensors is transmitted via wireless networks to centralized processing systems every few minutes, providing city authorities with an unprecedented level of detail about pollution patterns. This granular data reveals that pollution levels can vary dramatically even within a single neighborhood, depending on factors such as traffic density, building height, wind patterns, and proximity to industrial facilities. Such insights were impossible to obtain with older monitoring technologies and have proven invaluable in developing targeted pollution reduction strategies.

B. Artificial Intelligence and Machine Learning

The sheer volume of data generated by smart city sensors – often millions of data points per day – would be impossible for humans to analyze effectively. This is where artificial intelligence and machine learning algorithms become indispensable. These systems can identify patterns and correlations that would be invisible to human observers, such as the relationship between specific weather conditions, traffic patterns, and pollution spikes.

Predictive models developed through machine learning have become increasingly accurate at forecasting pollution events. By analyzing historical data alongside real-time information about traffic, weather, industrial activity, and even social events, these systems can predict with remarkable accuracy when and where pollution levels are likely to exceed safe thresholds. This predictive capability allows cities to implement proactive measures rather than simply responding to problems after they occur.

Some advanced systems employ neural networks that continuously learn and improve their predictions. In Beijing, for instance, an AI system has achieved prediction accuracy rates exceeding 85% for pollution levels up to 72 hours in advance. This has enabled authorities to implement graduated response systems, ranging from public advisories during moderate pollution events to temporary industrial shutdowns and vehicle restrictions during severe episodes.

C. Smart Traffic Management Systems

Vehicle emissions represent one of the largest sources of urban air pollution, making intelligent transportation systems a critical component of smart city pollution control. Modern traffic management platforms integrate data from multiple sources – including road sensors, traffic cameras, GPS data from vehicles, and even mobile phone signals – to create a real-time picture of traffic flow throughout the city.

These systems can dynamically adjust traffic signal timing to optimize flow, reducing the stop-start driving patterns that generate excessive emissions. When sensors detect traffic congestion forming, the system can redirect vehicles to alternative routes through variable message signs and navigation apps. Some cities have implemented adaptive traffic corridors where signal timing automatically adjusts to prioritize public transportation vehicles, encouraging citizens to choose buses and trains over private cars.

More sophisticated systems integrate pollution monitoring directly into traffic management decisions. If air quality sensors detect high pollution levels in a particular area, the traffic management system can automatically redirect vehicles away from that zone, implement congestion pricing, or increase tolls for high-emission vehicles. This creates a feedback loop where traffic patterns are continuously adjusted based on their environmental impact.

D. Building Management and Energy Efficiency

Buildings account for approximately 40% of urban energy consumption and a significant proportion of city pollution, particularly in cities where electricity is generated from fossil fuels. Smart city technologies address this through intelligent building management systems that optimize energy use throughout a structure’s lifecycle.

Smart meters and building sensors monitor energy consumption in real-time, identifying inefficiencies and automatically adjusting heating, ventilation, and air conditioning (HVAC) systems for optimal performance. These systems can anticipate building occupancy patterns, reducing energy use in unoccupied areas while ensuring comfort when spaces are in use. Integration with renewable energy sources such as solar panels and wind turbines allows buildings to generate their own clean power, with excess energy fed back into the city grid.

At the district level, some cities have implemented smart microgrids that connect multiple buildings, allowing them to share resources and balance energy loads. These microgrids can operate independently during peak demand periods, reducing strain on the main power grid and decreasing reliance on fossil fuel-based power plants that contribute to urban pollution.

E. Public Engagement Platforms

Technology alone cannot create truly smart cities; citizen participation is essential. Modern smart cities employ digital platforms that transform residents from passive recipients of services into active participants in pollution reduction efforts. Mobile applications provide personalized information about air quality in users’ specific locations, health recommendations based on pollution levels, and suggestions for reducing individual environmental footprints.

Some cities have gamified pollution reduction, creating competitive challenges between neighborhoods or offering rewards for environmentally friendly behaviors tracked through apps. Others use crowdsourcing to identify pollution sources, allowing citizens to report issues such as excessive vehicle emissions, industrial violations, or illegal burning that official sensors might miss.

The most advanced systems create feedback mechanisms where citizens can see the direct impact of their actions. When many people choose public transport on a high-pollution day, the system displays the collective reduction in emissions achieved, reinforcing positive behaviors and building a sense of community environmental responsibility.

Questions 14-26

Questions 14-18: Matching Headings

Choose the correct heading for sections A-E from the list of headings below.

List of Headings:
i. The role of citizens in reducing urban pollution
ii. Traditional monitoring methods and their limitations
iii. Advanced sensors creating detailed pollution maps
iv. Managing vehicle emissions through intelligent systems
v. Reducing building energy consumption with smart technology
vi. Using AI to predict and prevent pollution events
vii. The cost of implementing smart city technology
viii. International cooperation in pollution control

14. Section A
15. Section B
16. Section C
17. Section D
18. Section E

Questions 19-23: Summary Completion

Complete the summary below using words from the passage. Write NO MORE THAN TWO WORDS for each answer.

Modern smart cities use thousands of (19) throughout urban areas to measure various pollutants in the air. The data is sent via wireless networks to (20) where artificial intelligence analyzes millions of data points. In Beijing, an AI system achieves over 85% accuracy in predicting pollution levels up to (21) in advance. Traffic management systems reduce emissions by adjusting (22) to optimize vehicle flow. Buildings use intelligent systems to control their (23) automatically, significantly reducing energy waste.

Questions 24-26: Yes/No/Not Given

Do the following statements agree with the claims of the writer in the passage?

Write YES if the statement agrees with the claims of the writer, NO if the statement contradicts the claims of the writer, or NOT GIVEN if it is impossible to say what the writer thinks about this.

24. Smart city sensors are now cheaper and more accurate than traditional monitoring stations.

25. Machine learning algorithms can identify all pollution sources without any human intervention.

26. Smart microgrids can function independently from the main power grid during high-demand periods.

Cảm biến thông minh giám sát chất lượng không khí đô thị trong thành phố thông minh hiện đại

---

PASSAGE 3 – Challenges and Future Perspectives in Smart City Pollution Mitigation

Độ khó: Hard (Band 7.0-9.0)

Thời gian đề xuất: 23-25 phút

While the potential of smart cities to dramatically reduce urban pollution has been amply demonstrated in pilot projects and early adopters, the pathway to widespread implementation remains fraught with multifaceted challenges that extend well beyond mere technological considerations. The sociotechnical complexity inherent in transforming established urban environments into intelligent, environmentally responsive ecosystems demands careful examination of not only the technological infrastructure but also the institutional frameworks, economic models, privacy concerns, and social equity implications that accompany such profound urban transformation.

The economic barriers to smart city implementation represent perhaps the most immediately apparent obstacle. The initial capital investment required to deploy comprehensive sensor networks, upgrade communication infrastructure, implement AI-driven management systems, and retrofit existing buildings with intelligent controls can reach billions of dollars even for medium-sized cities. Copenhagen’s transition toward becoming carbon-neutral by 2025, for instance, has required investments exceeding €1 billion in smart infrastructure alone. This substantial financial burden poses particular challenges for cities in developing nations, where urgent pollution problems often coincide with limited budgets and competing development priorities. The return on investment timeline for smart city technologies typically extends over decades, creating fiscal constraints that discourage cash-strapped municipal governments from committing to such long-term projects despite their environmental benefits.

Moreover, the technological infrastructure necessary for effective smart city pollution management extends far beyond visible sensors and control systems. Cities must develop robust data networks capable of transmitting massive volumes of information with minimal latency, requiring investments in fiber optic networks, 5G wireless technology, and secure data centers. The interoperability of systems from different vendors presents additional complications; sensors from one manufacturer may not communicate effectively with management platforms from another, creating fragmented systems that fail to achieve the integrated functionality essential for optimal pollution control. Establishing standardized protocols and ensuring backward compatibility with legacy systems adds layers of complexity and expense to implementation efforts.

The governance challenges associated with smart city development prove equally daunting. Effective pollution management requires unprecedented coordination among multiple municipal departments – transportation, environmental protection, urban planning, energy, and public health – that traditionally operate in bureaucratic silos with limited communication. Creating the institutional frameworks necessary to facilitate data sharing, joint decision-making, and coordinated responses demands fundamental reorganization of municipal government structures, often encountering resistance from established departments protective of their autonomy and resources. Furthermore, smart city initiatives frequently require collaboration between public sector entities and private technology companies, raising questions about data ownership, commercial interests influencing public policy, and the appropriate balance between innovation and public control.

Privacy concerns constitute another significant impediment to smart city implementation. The comprehensive monitoring systems necessary for effective pollution management inevitably collect vast amounts of data about citizen movements, behaviors, and activities. While this data proves invaluable for optimizing traffic flow, predicting pollution hotspots, and evaluating policy effectiveness, it simultaneously creates potential for surveillance that many citizens find deeply troubling. The controversy surrounding smart streetlights in San Diego, which were discovered to collect detailed information about pedestrian movements beyond what was publicly disclosed, illustrates the tension between environmental objectives and privacy rights. Developing transparent data governance frameworks that clearly specify what information is collected, how it is used, who has access, and how long it is retained becomes essential for maintaining public trust, yet such frameworks remain underdeveloped in most jurisdictions.

The issue of social equity in smart city development deserves particular attention, as pollution reduction technologies risk exacerbating existing inequalities if not carefully managed. Wealthier neighborhoods often receive priority in sensor deployment and infrastructure upgrades, while disadvantaged communities – frequently those most affected by pollution due to proximity to industrial facilities and transportation corridors – may be excluded from smart city benefits. The digital divide further compounds these inequities; citizens lacking smartphones or reliable internet access cannot utilize apps providing air quality information or participate in crowdsourcing initiatives, effectively excluding them from the benefits of smart city technologies and diminishing their agency in environmental decision-making. Addressing these concerns requires deliberate policy interventions ensuring equitable distribution of smart city infrastructure and universal access to associated services.

Looking toward the future, the next generation of smart city technologies promises even more sophisticated pollution mitigation capabilities. Quantum computing may soon enable pollution models of unprecedented complexity and accuracy, incorporating countless variables to predict air quality with near-perfect precision weeks in advance. Autonomous vehicles, when widely deployed, could dramatically reduce traffic congestion and optimize routing in ways impossible with human drivers, potentially cutting transportation-related emissions by 40-60% according to some projections. Nanotechnology applications might enable sensors capable of detecting pollutants at molecular levels and even neutralizing certain contaminants in real-time.

Biotechnology integration represents another frontier in smart city pollution management. Some researchers envision bioengineered plants with enhanced capacity to absorb air pollutants deployed throughout urban environments, their health monitored by sensors to identify pollution hotspots. Synthetic biology might produce microorganisms capable of breaking down specific pollutants, released in controlled ways to address acute pollution episodes. While such technologies remain largely speculative, they indicate the expanding possibilities for biological and technological systems to work in synergy toward pollution reduction.

However, technology enthusiasts’ optimistic predictions must be tempered with realistic assessments of the social, political, and economic factors that will ultimately determine whether smart cities fulfill their potential for pollution reduction or become merely another technological panacea that fails to address underlying problems. The most successful smart cities will likely be those that view technology not as an end in itself but as a tool enabling broader social transformation toward sustainable urban living. This requires not only technological innovation but also changes in governance structures, economic incentives, cultural attitudes, and individual behaviors – transformations far more challenging than simply installing sensors and AI systems.

The concept of circular economy integrated with smart city infrastructure may offer a more holistic approach to pollution reduction. Rather than focusing solely on monitoring and managing pollution after it is produced, truly intelligent cities might reorganize production and consumption patterns to minimize waste generation in the first place. Smart systems could facilitate sharing economies that reduce overall consumption, optimize material flows to maximize recycling and reuse, and create closed-loop systems where one industry’s waste becomes another’s input, dramatically reducing the environmental footprint of urban life.

Ultimately, the question facing cities worldwide is not whether smart technologies can reduce urban pollution – pilot projects have convincingly demonstrated this capability – but whether societies will make the sustained commitments of resources, political will, and social adaptation necessary to implement these technologies equitably and effectively at scale. The answer will determine not merely the cleanliness of urban air but the habitability of cities for future generations and, given cities’ disproportionate contribution to global pollution, potentially the trajectory of climate change itself.

Questions 27-40

Questions 27-31: Multiple Choice

Choose the correct letter, A, B, C, or D.

27. According to the passage, the main economic challenge for smart cities is
A. the high cost of maintaining sensor networks
B. the substantial initial investment and long-term return period
C. the expense of training municipal employees
D. competition between cities for limited funding

28. Technological infrastructure challenges include all of the following EXCEPT
A. ensuring different vendors’ systems can work together
B. developing high-speed data networks
C. replacing all existing buildings with new structures
D. establishing standardized communication protocols

29. The governance challenges mentioned primarily involve
A. finding qualified technology experts
B. coordinating multiple government departments
C. competing with private companies
D. international regulatory compliance

30. The San Diego smart streetlights controversy illustrates
A. the failure of pollution monitoring technology
B. conflicts between environmental goals and privacy rights
C. problems with sensor accuracy
D. citizens’ rejection of all smart technology

31. According to the passage, quantum computing might contribute to pollution reduction by
A. replacing current sensor technologies
B. eliminating the need for AI systems
C. creating more accurate and complex pollution predictions
D. directly removing pollutants from the air

Questions 32-36: Matching Features

Match each description (32-36) with the correct technology or concept (A-H).

A. Autonomous vehicles
B. Nanotechnology
C. Bioengineered plants
D. Quantum computing
E. 5G wireless technology
F. Synthetic biology
G. Circular economy
H. Smart microgrids

32. Could reduce transportation emissions by 40-60%

33. Might enable sensors that detect pollution at molecular levels

34. Could involve enhanced vegetation that absorbs more air pollutants

35. May create microorganisms that decompose specific contaminants

36. Focuses on reorganizing production to minimize waste creation

Questions 37-40: Short-answer Questions

Answer the questions below using NO MORE THAN THREE WORDS from the passage for each answer.

37. What type of approach do researchers believe the most successful smart cities will adopt regarding technology?

38. What must change beyond just installing technology for smart cities to achieve true pollution reduction?

39. What type of economy could help cities minimize waste generation rather than just managing pollution?

40. According to the passage, what do pilot projects convincingly demonstrate about smart technologies?

Thách thức triển khai hệ thống giám sát môi trường và quản lý dữ liệu trong đô thị thông minh

---

Answer Keys – Đáp Án

PASSAGE 1: Questions 1-13

1. B
2. C
3. B
4. C
5. C
6. FALSE
7. TRUE
8. FALSE
9. FALSE
10. C
11. B
12. E
13. D

PASSAGE 2: Questions 14-26

14. iii
15. vi
16. iv
17. v
18. i
19. sensors
20. centralized (processing) systems / processing systems
21. 72 hours
22. traffic signal timing / signal timing
23. HVAC systems
24. YES
25. NOT GIVEN
26. YES

PASSAGE 3: Questions 27-40

27. B
28. C
29. B
30. B
31. C
32. A
33. B
34. C
35. F
36. G
37. holistic approach / broader (social) transformation
38. governance structures / economic incentives / cultural attitudes / individual behaviors (any three words from these options)
39. circular economy
40. pollution reduction capability / this capability

---

Giải Thích Đáp Án Chi Tiết

Passage 1 – Giải Thích

Câu 1: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: smart cities, primarily use technology
• Vị trí trong bài: Đoạn 1, dòng 3-5
• Giải thích: Câu “Smart cities use digital technology and data analytics to improve the quality and performance of urban services, reduce costs and resource consumption” được paraphrase thành “improve urban services and reduce resource use” ở đáp án B. Đáp án A sai vì không đề cập đến tăng mật độ dân số; C sai vì không thay thế hoàn toàn cơ sở hạ tầng; D sai vì không loại bỏ tất cả ô nhiễm mà chỉ giảm thiểu.

Câu 2: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: Singapore, sensors
• Vị trí trong bài: Đoạn 2, dòng 1-3
• Giải thích: Câu “These devices collect real-time data on everything from traffic flow to air quality” cho thấy cảm biến thu thập nhiều loại dữ liệu khác nhau, không chỉ giao thông (A sai) hay chất lượng không khí (B sai). D không được đề cập trong bài.

Câu 6: FALSE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: technology, only react, after pollution occurs
• Vị trí trong bài: Đoạn 4, dòng 1-2
• Giải thích: Câu “One of the most innovative aspects of smart city technology is its ability to predict and prevent pollution problems rather than simply reacting to them” trực tiếp mâu thuẫn với nhận định trong câu hỏi. Công nghệ có thể dự đoán và phòng ngừa, không chỉ phản ứng sau khi xảy ra.

Câu 7: TRUE

• Dạng câu hỏi: True/False/Not Given
• Từ khóa: Amsterdam, citizens, access, mobile applications
• Vị trí trong bài: Đoạn 6, dòng 2-3
• Giải thích: Câu “Citizens can access this data through mobile apps” hoàn toàn khớp với thông tin trong câu hỏi.

Câu 10: C (Copenhagen)

• Dạng câu hỏi: Matching Information
• Từ khóa: algorithms, predict, future air quality
• Vị trí trong bài: Đoạn 4, dòng 2-3
• Giải thích: “Copenhagen, Denmark, city planners use predictive algorithms to forecast air quality levels up to 48 hours in advance” là bằng chứng rõ ràng.

Passage 2 – Giải Thích

Câu 14: iii

• Dạng câu hỏi: Matching Headings
• Từ khóa: Section A, sensors, pollution maps
• Vị trí trong bài: Section A, toàn bộ
• Giải thích: Section A tập trung vào “sensor revolution” và việc các cảm biến tạo ra “comprehensive, three-dimensional map of air quality” – tương ứng với heading “Advanced sensors creating detailed pollution maps”.

Câu 15: vi

• Dạng câu hỏi: Matching Headings
• Từ khóa: Section B, AI, predict, prevent
• Vị trí trong bài: Section B, toàn bộ
• Giải thích: Toàn bộ section B nói về “Artificial Intelligence and Machine Learning”, “predictive models”, và “proactive measures” – khớp với heading “Using AI to predict and prevent pollution events”.

Câu 19: sensors

• Dạng câu hỏi: Summary Completion
• Từ khóa: thousands of, throughout urban areas, measure pollutants
• Vị trí trong bài: Section A, đoạn 1, dòng 1-2
• Giải thích: “Modern smart cities deploy thousands of low-cost, high-precision sensors” – từ “sensors” là đáp án chính xác.

Câu 21: 72 hours

• Dạng câu hỏi: Summary Completion
• Từ khóa: Beijing, 85% accuracy, predicting
• Vị trí trong bài: Section B, đoạn cuối, dòng 2-3
• Giải thích: “An AI system has achieved prediction accuracy rates exceeding 85% for pollution levels up to 72 hours in advance” – “72 hours” là đáp án.

Câu 24: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: sensors, cheaper, more accurate, traditional monitoring stations
• Vị trí trong bài: Section A, đoạn 1
• Giải thích: “Low-cost, high-precision sensors” và “Unlike traditional monitoring stations, which are expensive and limited in number” cho thấy cảm biến mới rẻ hơn và chính xác hơn.

Câu 26: YES

• Dạng câu hỏi: Yes/No/Not Given
• Từ khóa: smart microgrids, function independently, high-demand periods
• Vị trí trong bài: Section D, đoạn cuối, dòng 2-3
• Giải thích: “These microgrids can operate independently during peak demand periods” trực tiếp xác nhận nhận định trong câu hỏi.

Passage 3 – Giải Thích

Câu 27: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: main economic challenge
• Vị trí trong bài: Đoạn 2, dòng 1-5
• Giải thích: “The initial capital investment required… can reach billions of dollars” và “The return on investment timeline… extends over decades” cho thấy thách thức chính là vốn đầu tư lớn và thời gian hoàn vốn dài, tương ứng với đáp án B.

Câu 28: C

• Dạng câu hỏi: Multiple Choice
• Từ khóa: technological infrastructure challenges, EXCEPT
• Vị trí trong bài: Đoạn 3, toàn bộ
• Giải thích: Đoạn văn đề cập A (interoperability), B (data networks), D (standardized protocols), nhưng không đề cập việc thay thế tất cả các tòa nhà hiện có bằng công trình mới (C).

Câu 30: B

• Dạng câu hỏi: Multiple Choice
• Từ khóa: San Diego, smart streetlights, controversy
• Vị trí trong bài: Đoạn 5, dòng 4-6
• Giải thích: “The controversy… illustrates the tension between environmental objectives and privacy rights” trực tiếp nêu rõ xung đột giữa mục tiêu môi trường và quyền riêng tư.

Câu 32: A (Autonomous vehicles)

• Dạng câu hỏi: Matching Features
• Từ khóa: reduce transportation emissions, 40-60%
• Vị trí trong bài: Đoạn 7, dòng 2-4
• Giải thích: “Autonomous vehicles… potentially cutting transportation-related emissions by 40-60%” khớp chính xác.

Câu 36: G (Circular economy)

• Dạng câu hỏi: Matching Features
• Từ khóa: reorganizing production, minimize waste creation
• Vị trí trong bài: Đoạn 10, dòng 2-4
• Giải thích: “Circular economy… reorganize production and consumption patterns to minimize waste generation in the first place” là bằng chứng rõ ràng.

Câu 37: holistic approach

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: most successful smart cities, adopt regarding technology
• Vị trí trong bài: Đoạn 9, dòng 4-5
• Giải thích: “The concept of circular economy integrated with smart city infrastructure may offer a more holistic approach” – “holistic approach” là đáp án chính xác với không quá 3 từ.

Câu 40: pollution reduction capability

• Dạng câu hỏi: Short-answer Questions
• Từ khóa: pilot projects, convincingly demonstrate
• Vị trí trong bài: Đoạn cuối, dòng 1-2
• Giải thích: “Pilot projects have convincingly demonstrated this capability” – “this capability” được thay thế bằng “pollution reduction capability” từ câu trước.

Hệ thống quản lý giao thông thông minh giúp giảm ô nhiễm không khí từ phương tiện đô thị

---

Từ Vựng Quan Trọng Theo Passage

Passage 1 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
smart cities	n	/smɑːt ˈsɪtiz/	thành phố thông minh	Smart cities use digital technology to improve urban services	smart city development, smart city technology
infrastructure	n	/ˈɪnfrəstrʌktʃə(r)/	cơ sở hạ tầng	The strain on infrastructure becomes increasingly severe	urban infrastructure, transport infrastructure
data analytics	n	/ˈdeɪtə ænəˈlɪtɪks/	phân tích dữ liệu	Use data analytics to improve performance	big data analytics, real-time analytics
sensors	n	/ˈsensə(r)z/	cảm biến, thiết bị cảm ứng	Sensors detect unusually high levels of particulate matter	install sensors, sensor network
particulate matter	n	/pɑːˈtɪkjələt ˈmætə(r)/	bụi mịn, chất hạt lơ lửng	Sensors detect high levels of particulate matter	fine particulate matter, PM2.5
renewable energy	n	/rɪˈnjuːəbl ˈenədʒi/	năng lượng tái tạo	Barcelona focused on renewable energy integration	renewable energy sources, clean renewable energy
predictive algorithms	n	/prɪˈdɪktɪv ˈælɡərɪðəmz/	thuật toán dự đoán	Use predictive algorithms to forecast air quality	advanced predictive algorithms
carbon footprint	n	/ˈkɑːbən ˈfʊtprɪnt/	dấu chân carbon (lượng khí thải CO2)	Reduces the carbon footprint associated with urban lighting	reduce carbon footprint, lower carbon footprint
Internet of Things	n	/ˈɪntənet əv θɪŋz/	Internet vạn vật (IoT)	The Internet of Things plays a crucial role	IoT devices, IoT network
waste management	n	/weɪst ˈmænɪdʒmənt/	quản lý chất thải	Waste management has been revolutionized	smart waste management, efficient waste management
citizen engagement	n	/ˈsɪtɪzn ɪnˈɡeɪdʒmənt/	sự tham gia của công dân	Success depends on citizen engagement	increase citizen engagement, promote citizen engagement
traffic congestion	n	/ˈtræfɪk kənˈdʒestʃən/	tắc nghẽn giao thông	Smart parking reduces traffic congestion	reduce traffic congestion, ease traffic congestion

Passage 2 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
sophisticated	adj	/səˈfɪstɪkeɪtɪd/	tinh vi, phức tạp	Requires a sophisticated technological infrastructure	sophisticated technology, sophisticated system
integrated network	n	/ˈɪntɪɡreɪtɪd ˈnetwɜːk/	mạng lưới tích hợp	An integrated network of sensors and AI	fully integrated network
artificial intelligence	n	/ɑːtɪˈfɪʃl ɪnˈtelɪdʒəns/	trí tuệ nhân tạo (AI)	Artificial intelligence that works together	AI system, AI technology
granular data	n	/ˈɡrænjələ(r) ˈdeɪtə/	dữ liệu chi tiết, dữ liệu hạt	This granular data reveals pollution patterns	highly granular data
machine learning	n	/məˈʃiːn ˈlɜːnɪŋ/	học máy	Machine learning algorithms become indispensable	machine learning models, advanced machine learning
neural networks	n	/ˈnjʊərəl ˈnetwɜːks/	mạng nơ-ron	Neural networks continuously learn and improve	deep neural networks, artificial neural networks
proactive measures	n	/prəʊˈæktɪv ˈmeʒə(r)z/	biện pháp chủ động	Allows cities to implement proactive measures	take proactive measures
intelligent transportation	n	/ɪnˈtelɪdʒənt trænspɔːˈteɪʃn/	vận tải thông minh	Intelligent transportation systems are critical	intelligent transportation system (ITS)
congestion pricing	n	/kənˈdʒestʃən ˈpraɪsɪŋ/	định giá tắc nghẽn	Can implement congestion pricing	introduce congestion pricing
feedback loop	n	/ˈfiːdbæk luːp/	vòng phản hồi	Creates a feedback loop	positive feedback loop, continuous feedback loop
HVAC systems	n	/eɪtʃ viː eɪ siː ˈsɪstəmz/	hệ thống sưởi, thông gió và điều hòa	Automatically adjusting HVAC systems	energy-efficient HVAC systems
microgrids	n	/ˈmaɪkrəʊɡrɪdz/	vi lưới điện	Smart microgrids connect multiple buildings	smart microgrids, local microgrids
crowdsourcing	n	/ˈkraʊdsɔːsɪŋ/	huy động nguồn lực từ cộng đồng	Use crowdsourcing to identify pollution sources	crowdsourcing platform
environmental footprint	n	/ɪnvaɪrənˈmentl ˈfʊtprɪnt/	dấu chân môi trường	Suggestions for reducing environmental footprints	reduce environmental footprint
gamified	v	/ˈɡeɪmɪfaɪd/	trò chơi hóa	Some cities have gamified pollution reduction	gamified experience, gamified approach

Passage 3 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
multifaceted	adj	/ˌmʌltiˈfæsɪtɪd/	nhiều khía cạnh, đa diện	Fraught with multifaceted challenges	multifaceted approach, multifaceted problem
sociotechnical	adj	/ˌsəʊsiəʊˈteknɪkl/	xã hội-kỹ thuật	The sociotechnical complexity inherent	sociotechnical system
institutional frameworks	n	/ˌɪnstɪˈtjuːʃənl ˈfreɪmwɜːks/	khung thể chế	Institutional frameworks accompanying transformation	establish institutional frameworks
social equity	n	/ˈsəʊʃl ˈekwəti/	công bằng xã hội	Social equity implications	promote social equity, ensure social equity
capital investment	n	/ˈkæpɪtl ɪnˈvestmənt/	đầu tư vốn	Initial capital investment can reach billions	large capital investment, substantial capital investment
fiscal constraints	n	/ˈfɪskl kənˈstreɪnts/	hạn chế tài chính	Creating fiscal constraints	severe fiscal constraints, tight fiscal constraints
interoperability	n	/ˌɪntərɒpərəˈbɪləti/	khả năng tương tác	The interoperability of systems	system interoperability, data interoperability
fragmented systems	n	/fræɡˈmentɪd ˈsɪstəmz/	hệ thống phân mảnh	Creating fragmented systems	highly fragmented systems
bureaucratic silos	n	/bjʊərəˈkrætɪk ˈsaɪləʊz/	các đơn vị hành chính độc lập	Operate in bureaucratic silos	break down bureaucratic silos
data governance	n	/ˈdeɪtə ˈɡʌvənəns/	quản trị dữ liệu	Transparent data governance frameworks	data governance policy, robust data governance
surveillance	n	/sɜːˈveɪləns/	giám sát	Creates potential for surveillance	mass surveillance, government surveillance
digital divide	n	/ˈdɪdʒɪtl dɪˈvaɪd/	khoảng cách số	The digital divide compounds inequities	bridge the digital divide, narrow the digital divide
quantum computing	n	/ˈkwɒntəm kəmˈpjuːtɪŋ/	máy tính lượng tử	Quantum computing may enable complex models	quantum computing technology
autonomous vehicles	n	/ɔːˈtɒnəməs ˈviːɪklz/	xe tự hành	Autonomous vehicles could reduce emissions	fully autonomous vehicles
nanotechnology	n	/ˌnænəʊtekˈnɒlədʒi/	công nghệ nano	Nanotechnology applications might enable sensors	advanced nanotechnology
bioengineered	adj	/ˌbaɪəʊendʒɪˈnɪəd/	được thiết kế sinh học	Bioengineered plants with enhanced capacity	bioengineered organisms
circular economy	n	/ˈsɜːkjələ(r) ɪˈkɒnəmi/	kinh tế tuần hoàn	Circular economy integrated with smart infrastructure	transition to circular economy
closed-loop systems	n	/kləʊzd luːp ˈsɪstəmz/	hệ thống khép kín	Create closed-loop systems	closed-loop production system
habitability	n	/ˌhæbɪˈtæbələti/	tính có thể sinh sống được	The habitability of cities for future generations	urban habitability

Tương lai bền vững của thành phố thông minh với công nghệ xanh và chất lượng sống cao

---

Kết Bài

Chủ đề về thành phố thông minh và giảm thiểu ô nhiễm đô thị không chỉ là một xu hướng trong đề thi IELTS Reading mà còn phản ánh một trong những thách thức quan trọng nhất của thế giới hiện đại. Qua bộ đề thi mẫu này, bạn đã được trải nghiệm đầy đủ ba mức độ khó từ cơ bản đến nâng cao, với tổng cộng 40 câu hỏi đa dạng hoàn toàn giống với format thi thật.

Passage 1 giới thiệu các khái niệm cơ bản về thành phố thông minh và các giải pháp công nghệ đơn giản, phù hợp cho học viên band 5.0-6.5. Passage 2 đi sâu vào công nghệ đằng sau các hệ thống quản lý ô nhiễm với từ vựng học thuật và cấu trúc câu phức tạp hơn, thách thức học viên ở mức band 6.0-7.5. Cuối cùng, Passage 3 phân tích những thách thức và triển vọng tương lai với độ khó band 7.0-9.0, đòi hỏi khả năng phân tích và suy luận cao.

Phần đáp án chi tiết với giải thích cụ thể về vị trí thông tin, cách paraphrase và lý do các đáp án đúng/sai sẽ giúp bạn hiểu sâu hơn về cách làm bài hiệu quả. Đừng quên ôn tập bảng từ vựng quan trọng – đây là những từ khóa xuất hiện thường xuyên không chỉ trong chủ đề này mà còn trong nhiều bài đọc khác về môi trường và công nghệ.

Hãy luyện tập đề này nhiều lần, mỗi lần tập trung vào một kỹ năng cụ thể: lần đầu làm đúng thời gian 60 phút, lần sau phân tích kỹ thuật paraphrase, lần tiếp theo học từ vựng và cấu trúc câu. Sự kiên trì và phương pháp học đúng sẽ giúp bạn đạt được band điểm mục tiêu trong kỳ thi IELTS sắp tới.